Microbiome at the Frontier of Personalized Medicine

Gallic acid ameliorates synovial inflammation and fibrosis by regulating the intestinal flora and its metabolites

Gallic acid (GA) has been found by a large number of studies to have pharmacological effects such as antioxidant and anti-inflammatory properties. However, the underlying therapeutic mechanisms are not fully understood.. Studies have shown that altering the intestinal flora affects host metabolism and effectively mediates the development of synovitis. The aim of this study was to explore the pharmacological effects of GA in the treatment of synovial inflammation and anti-synovial fibrosis in knee osteoarthritis (KOA) and the underlying mechanisms by macrogenomics combined with off-target metabolomics. We established a synovitis model via in vivo and in vitro experiments to observe the effect of GA intervention on synovitis. Moreover, we collected serum and feces from rats and analyzed the changes in intestinal flora by macro-genome sequencing and the changes in metabolites in the serum by untargeted metabolomics. We found that GA reduced the levels of IL-1β, IL-6, and TNF-α, and decreased the protein expression levels of α-SMA, TGF-β, and Collagen I in synovial tissues and cells, and the composition and function of the intestinal flora were similarly altered. Combined with macrogenomic pathway enrichment analysis and metabolic pathway enrichment analysis, these findings revealed that GA impacts Bacteroidia and Muribaculaceae abundance, and via the following metabolic pathways: sphingolipid metabolism, glycerophospholipid metabolism, and arginine biology.to ameliorate synovial inflammation and fibrosis in KOA. The therapeutic effect of GA on KOA synovitis and fibrosis is partly attributed to the alleviation of metabolic disorder and the rebalancing of the intestinal flora. These results provides a rationale for the therapeutic application of GA in the treatment of synovitis.

How Diet and Lifestyle Can Fine-Tune Gut Microbiomes for Healthy Aging

Many physical, social, and psychological changes occur during aging that raise the risk of developing chronic diseases, frailty, and dependency. These changes adversely affect the gut microbiota, a phenomenon known as microbe-aging. Those microbiota alterations are, in turn, associated with the development of age-related diseases. The gut microbiota is highly responsive to lifestyle and dietary changes, displaying a flexibility that also provides anactionable tool by which healthy aging can be promoted. This review covers, firstly, the main lifestyle and socioeconomic factors that modify the gut microbiota composition and function during healthy or unhealthy aging and, secondly, the advances being made in defining and promoting healthy aging, including microbiome-informed artificial intelligence tools, personalized dietary patterns, and food probiotic systems.

HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations

Accumulating clinical studies have consistently demonstrated that the microbes in the human body closely interact with the human host, actively participating in the regulation of drug effectiveness. Identifying the associations between microbes and drugs can facilitate the development of drug discovery, and microbes have become a new target in antimicrobial drug development. However, the discovery of microbe-drug associations relies on clinical or biological experiments, which are not only time-consuming but also financially burdensome. Thus, the utilization of computational methods to predict microbe-drug associations holds promise for reducing costs and enhancing the efficiency of biological experiments. Here, we introduce a new computational method, called HKFGCN (Heterogeneous information Kernel Fusion Graph Convolution Network), to predict the microbe-drug associations. Instead of extracting feature from a single network in previous studies, HKFGCN separately extracts topological information features from different networks, and further refines them by generating Gaussian kernel features. HKFGCN consists of three main steps. Firstly, we constructed two similarity networks and a microbe-drug association network based on numerous biological data. Second, we employed two types of encoders to extract features from these networks. Next, Gaussian kernel features were obtained from the drug and microbe features at each layer. Finally, we reconstructed the bipartite microbe-drug graph based on the learned representations. Experimental results demonstrate the excellent performance of the HKFGCN model across different datasets using the cross-validation scheme. Additionally, we conduced case studies on human immunodeficiency virus, and the results were corroborated by existing literatures. The prediction model's code is available at https://github.com/roll-of-bubble/HKFGCN.

Network impact of a single-time-point microbial sample

The human microbiome plays a crucial role in determining our well-being and can significantly influence human health. The individualized nature of the microbiome may reveal host-specific information about the health state of the subject. In particular, the microbiome is an ecosystem shaped by a tangled network of species-species and host-species interactions. Thus, analysis of the ecological balance of microbial communities can provide insights into these underlying interrelations. However, traditional methods for network analysis require many samples, while in practice only a single-time-point microbial sample is available in clinical screening. Recently, a method for the analysis of a single-time-point sample, which evaluates its 'network impact' with respect to a reference cohort, has been applied to analyze microbial samples from women with Gestational Diabetes Mellitus. Here, we introduce different variations of the network impact approach and systematically study their performance using simulated 'samples' fabricated via the Generalized Lotka-Volttera model of ecological dynamics. We show that the network impact of a single sample captures the effect of the interactions between the species, and thus can be applied to anomaly detection of shuffled samples, which are 'normal' in terms of species abundance but 'abnormal' in terms of species-species interrelations. In addition, we demonstrate the use of the network impact in binary and multiclass classifications, where the reference cohorts have similar abundance profiles but different species-species interactions. Individualized analysis of the human microbiome has the potential to improve diagnosis and personalized treatments.

The Potential Role of Intestinal Stem Cells and Microbiota for the Treatment of Colorectal Cancer

Colorectal cancer is a global health concern with high incidence and mortality rates. Conventional treatments like surgery, chemotherapy, and radiation therapy have limitations in improving patient survival rates. Recent research highlights the role of gut microbiota and intestinal stem cells in maintaining intestinal health and their potential therapeutic applications in colorectal cancer treatment. The interaction between gut microbiota and stem cells influences epithelial self-renewal and overall intestinal homeostasis. Novel therapeutic approaches, including immunotherapy, targeted therapy, regenerative medicine using stem cells, and modulation of gut microbiota, are being explored to improve treatment outcomes. Accordingly, this chapter provides an overview of the potential therapeutic applications of gut microbiota and intestinal stem cells in treating colorectal cancer.

Interactions-based classification of a single microbial sample

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

Effect of gut microbiome on serotonin metabolism: a personalized treatment approach

Several factors including diet, exercise, and medications influence the makeup of the resilient but adaptable gut microbiome. Bacteria in the gut have a significant role in the homeostasis of the neurotransmitter serotonin, also known as 5-hydroxytryptamine, involved in mood and behavior. The goal of the current work is to review the effect of the gut microbiome on serotonin metabolism, and how it can potentially contribute to the development of a personalized treatment approach for depression and anxiety. Bacterial strains provide innovative therapeutic targets that can be used for disorders, such as depression, that involve dysregulation of serotonin. Advances in bacterial genomic sequencing have increased the accessibility and affordability of microbiome testing, which unlocks a new targeted pathway to modulate serotonin metabolism by targeting the gut-brain axis. Microbiome testing can facilitate the recommendation of strain-specific probiotic supplements based on patient-specific microbial profiles. Several studies have shown that supplementation with probiotics containing specific species of bacteria, such as Bifidobacterium and Lactobacillus, can improve symptoms of depression. Further research is needed to improve the process and interpretation of microbiome testing and how to successfully incorporate testing results into guiding clinical decision-making. This targeted approach centered around the gut-brain axis can provide a novel way to personalize therapy for mental health disorders.

From the Gut to the Brain: Is Microbiota a New Paradigm in Parkinson's Disease Treatment?

Parkinson's disease (PD) is recognized as the second most prevalent primary chronic neurodegenerative disorder of the central nervous system. Clinically, PD is characterized as a movement disorder, exhibiting an incidence and mortality rate that is increasing faster than any other neurological condition. In recent years, there has been a growing interest concerning the role of the gut microbiota in the etiology and pathophysiology of PD. The establishment of a brain-gut microbiota axis is now real, with evidence denoting a bidirectional communication between the brain and the gut microbiota through metabolic, immune, neuronal, and endocrine mechanisms and pathways. Among these, the vagus nerve represents the most direct form of communication between the brain and the gut. Given the potential interactions between bacteria and drugs, it has been observed that the therapies for PD can have an impact on the composition of the microbiota. Therefore, in the scope of the present review, we will discuss the current understanding of gut microbiota on PD and whether this may be a new paradigm for treating this devastating disease.

Annotating microbial functions with ProkFunFind

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Microbiome Dynamics: A Paradigm Shift in Combatting Infectious Diseases

Infectious diseases have long posed a significant threat to global health and require constant innovation in treatment approaches. However, recent groundbreaking research has shed light on a previously overlooked player in the pathogenesis of disease-the human microbiome. This review article addresses the intricate relationship between the microbiome and infectious diseases and unravels its role as a crucial mediator of host-pathogen interactions. We explore the remarkable potential of harnessing this dynamic ecosystem to develop innovative treatment strategies that could revolutionize the management of infectious diseases. By exploring the latest advances and emerging trends, this review aims to provide a new perspective on combating infectious diseases by targeting the microbiome.

DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction

The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the screening of MDAs at a low cost. Most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. However, different calculation methods result in distinct similarity matrices, and message passing in graph neural networks (GNNs) induces phenomena of over-smoothing and over-squashing, thereby impacting the performance of the model. To address these issues, we proposed a novel graph representation learning model, dual-modal graph learning for microbe-drug association prediction (DMGL-MDA). It comprises a dual-modal embedding module, a bipartite graph network embedding module, and a predictor module. To assess the performance of DMGL-MDA, we compared it against state-of-the-art methods using two benchmark datasets. Through cross-validation, we illustrated the superiority of DMGL-MDA. Furthermore, we conducted ablation experiments and case studies to validate the effective performance of the model.

Modulation of the Gut Microbiota by Nutrition and Its Relationship to Epigenetics

The intestinal microbiota is a community of microorganisms inhabiting the human intestines, potentially influencing both physiological and pathophysiological processes in the human body. Existing evidence suggests that nutrients can influence the modulation of the gut microbiota. However, there is still limited evidence regarding the effects of vitamin and mineral supplementation on the human gut microbiota through epigenetic modification. It is plausible that maintaining an adequate dietary intake of vitamin D, iron, fibre, zinc and magnesium may have a beneficial effect on alleviating inflammation in the body, reducing oxidative stress, and improving the condition of the intestinal microbiota through various epigenetic mechanisms. Moreover, epigenetics involves alterations in the phenotype of a cell without changing its fundamental DNA sequence. It appears that the modulation of the microbiota by various nutrients may lead to epigenetic regulation. The correlations between microbiota and epigenetics are potentially interdependent. Therefore, the primary objective of this review is to identify the complex relationships between diet, gut microbiota, and epigenetic regulation. These interactions could play a crucial role in systemic health.

MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec

Introduction

Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes.

Methods

In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions.

Results and discussion

Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

Gut-joint axis: Oral Probiotic ameliorates Osteoarthritis

Osteoarthritis (OA) etiology is multifactorial, and its prevalence is growing globally. The Gut microbiota shapes our immune system and impacts all aspects of health and disease. The idea of utilizing probiotics to treat different conditions prevails. Concerning musculoskeletal illness and health, current data lack the link to understand the interactions between the host and microbiome. We report that S. thermophilus, L. pentosus (as probiotics), and γ-aminobutyric acid (GABA) harbour against osteoarthritis in vivo and alleviate IL-1β induced changes in chondrocytes in vitro. We examined the increased GABA concentration in mice's serum and small intestine content followed by bacterial treatment. The treatment inhibited the catabolism of cartilage and rescued mice joints from degradation. Furthermore, the anabolic markers upregulated and decreased inflammatory markers in mice knee joints and chondrocytes. This study is the first to represent GABA's chondrogenic and chondroprotective effects on joints and human chondrocytes. This data provides a foundation for future studies to elucidate the role of GABA in regulating chondrocyte cell proliferation. These findings opened future horizons to understanding the gut-joint axis and OA treatment. Thus, probiotic/GABA therapy shields OA joints in mice and could at least serve as adjuvant therapy to treat osteoarthritis.

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

The symbiosis of probiotic bacteria with humans has rendered various health benefits while providing nutrition and a suitable environment for their survival. However, the probiotics must survive unfavorable gut conditions to exert beneficial effects. The intrinsic resistance of probiotics to survive harsh conditions results from a myriad of proteins. Interaction of microbial proteins with the host is indispensable for modulating the gut microbiome, such as interaction with cell receptors and protective action against pathogens. The complex interplay of proteins should be unraveled by utilizing metaproteomic strategies. The contribution of probiotics to health is now widely accepted. However, due to the inconsistency of generalized probiotics, contemporary research toward precision probiotics has gained momentum for customized treatment. This review explores the application of metaproteomics and AI/ML algorithms in resolving multiomics data analysis and in silico prediction of microbial features for screening specific beneficial probiotic organisms. Implementing these integrative strategies could augment the potential of precision probiotics for personalized healthcare.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Microbial Dysbiosis and Male Infertility: Understanding the Impact and Exploring Therapeutic Interventions

The human microbiota in the genital tract is pivotal for maintaining fertility, but its disruption can lead to male infertility. This study examines the relationship between microbial dysbiosis and male infertility, underscoring the promise of precision medicine in this field. Through a comprehensive review, this research indicates microbial signatures associated with male infertility, such as altered bacterial diversity, the dominance of pathogenic species, and imbalances in the genital microbiome. Key mechanisms linking microbial dysbiosis to infertility include inflammation, oxidative stress, and sperm structural deterioration. Emerging strategies like targeted antimicrobial therapies, probiotics, prebiotics, and fecal microbiota transplantation have shown potential in adjusting the genital microbiota to enhance male fertility. Notably, the application of precision medicine, which customizes treatments based on individual microbial profiles and specific causes of infertility, emerges as a promising approach to enhance treatment outcomes. Ultimately, microbial dysbiosis is intricately linked to male infertility, and embracing personalized treatment strategies rooted in precision medicine principles could be the way forward in addressing infertility associated with microbial factors.

Altered gut microbiota richness in individuals with a history of lateral ankle sprain

This study aimed to examine differences in the intestinal microbiota diversity in individuals with and without a history of a lateral ankle sprain (LAS). Fifty male college student athletes with (n=32) and without (n=18) a LAS history participated in this study. Faecal samples were collected in the morning after awakening during an off-season, and faecal microbiota were characterized via bacteria 16S rRNA amplicon sequencing. Alpha-diversity metrics and ß-diversity indices were calculated to assess the gut microbiota diversity. The LAS-history group significantly had lower Chao1 (p=0.020) and abundance-based coverage estimators (p=0.035) indices compared to the control group. Gut microbiota composition was not significantly different between athletes with a LAS history and controls (R2 =0.01, p 0.414). Athletes with a history of LASs had significantly higher proportions of Bacteroides Fragilis (p=0.024) and Ruminococcus Gnavus (p=0.021) compared with controls. The gut microbiota of athletes with a LAS history had less richness compared to controls, indicating potential associations between a LAS and the gut microbiota. This study highlights the potential link of a LAS to global health. This study may help raise awareness of strategies to prevent long-term health-related negative consequences in people suffering from LASs.

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.

Gut Microbiota Modulation of Short Bowel Syndrome and the Gut-Brain Axis

Short bowel syndrome (SBS) is a condition that results from a reduction in the length of the intestine or its functional capacity. SBS patients can have significant side effects and complications, the etiology of which remains ill-defined. Thus, facilitating intestinal adaptation in SBS remains a major research focus. Emerging data supports the role of the gut microbiome in modulating disease progression. There has been ongoing debate on defining a "healthy" gut microbiome, which has led to many studies analyzing the bacterial composition and shifts that occur in gastrointestinal disease states such as SBS and the resulting systemic effects. In SBS, it has also been found that microbial shifts are highly variable and dependent on many factors, including the anatomical location of bowel resection, length, and structure of the remnant bowel, as well as associated small intestinal bacterial overgrowth (SIBO). Recent data also notes a bidirectional communication that occurs between enteric and central nervous systems called the gut-brain axis (GBA), which is regulated by the gut microbes. Ultimately, the role of the microbiome in disease states such as SBS have many clinical implications and warrant further investigation. The focus of this review is to characterize the role of the gut microbiota in short bowel syndrome and its impact on the GBA, as well as the therapeutic potential of altering the microbiome.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.

In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy

Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.

Effects of topical fluoride application on oral microbiota in young children with severe dental caries

While the effect of fluoride on severe early childhood caries (S-ECC) is clear, knowledge of how it influences the oral microbiota and the consequential effects on oral health is limited. In this cohort study, we investigated the changes introduced in the oral ecosystem before and after using fluoride varnish in 54- to 66-month-old individuals (n=90: 18 children were sampled at 5 different time points). 16S rDNA was amplified from bacterial samples using polymerase chain reaction, and high-throughput sequencing was performed using Illumina MiSeq platforms. Many pronounced microbial changes were related to the effects of fluoride varnishing. The health-associated Bacteroides and Uncultured_bacterium_f_Enterobacteriaceae were enriched in the saliva microbiome following treatment with fluoride varnishing. Co-occurrence network analysis of the dominant genera showed that different groups clearly showed different bacterial correlations. The PICRUSt algorithm was used to predict the function of the microbial communities from saliva samples. The results showed that starch and sucrose metabolism was greater after fluoride use. BugBase was used to determine phenotypes present in microbial community samples. The results showed that Haemophilus and Neisseria (phylum Proteobacteria) was greater before fluoride use. We conclude that the changes in oral microbiology play a role in fluoride prevention of S-ECC.

Microbiota as a new pathogenetic factor in the development of chronic hyperuricemia and gout. Part 2: gout therapy and the gut microbiota

The article presents current data on the effect of drugs for the treatment of gout on the composition and function of the intestinal microbiota. The potential possibilities of pre- and probiotics use for the prevention and complex therapy of gout are discussed, therapeutic effect may be associated with their impact on the uric acid synthesis and intestinal excretion, as well as with anti-inflammatory properties. The need for further research in this area is emphasized.

Gut-Brain Axis, Neurodegeneration and Mental Health: A Personalized Medicine Perspective

Neurological conditions such as neurodegenerative diseases and mental health disorders are a result of multifactorial underpinnings, leading to individual-based complex phenotypes. Demystification of these multifactorial connections will promote disease diagnosis and treatment. Personalized treatment rather than a one-size-fits-all approach would enable us to cater to the unmet healthcare needs based on protein-protein and gene-environment interactions. Gut-brain axis, as the name suggests, is a two-way biochemical communication pathway between the central nervous system (CNS) and enteric nervous system (ENS), enabling a mutual influence between brain and peripheral intestinal functions. The gut microbiota is a major component of this bidirectional communication, the composition of which is varied depending on the age, and disease conditions, among other factors. Gut microbiota profile is typically unique and personalized therapeutic intervention can aid in treating or delaying neurodegeneration and mental health conditions. Besides, research on the gut microbial influence on these conditions is gaining attention, and a better understanding of this concept can lead to identification of novel targeted therapies.

Graphical Abstract

Systems to model the personalized aspects of microbiome health and gut dysbiosis

The human gut microbiome is a complex and dynamic microbial entity that interacts with the environment and other parts of the body including the brain, heart, liver, and immune system. These multisystem interactions are highly conserved from invertebrates to humans, however the complexity and diversity of human microbiota compositions often yield a context that is unique to each individual. Yet commonalities remain across species, where a healthy gut microbiome will be rich in symbiotic commensal biota while an unhealthy gut microbiota will be experiencing abnormal blooms of pathobiont bacteria. In this review we discuss how omics technologies can be applied in a personalized approach to understand the microbial crosstalk and microbial-host interactions that affect the delicate balance between eubiosis and dysbiosis in an individual gut microbiome. We further highlight the strengths of model organisms in identifying and characterizing these conserved synergistic and/or pathogenic host-microbe interactions. And finally, we touch upon the growing area of personalized therapeutic interventions targeting gut microbiome.

Impact of the Ileal Microbiota on Surgical Site Infections in Crohn's Disease: A Nationwide Prospective Cohort

BACKGROUND AND AIMS

Surgery is performed in 50-70% of Crohn's disease [CD] patients, and its main risk is surgical site infection [SSI]. The microbiota has been extensively assessed in CD but not as a potential risk factor for septic morbidity. The objective of this study was to assess the impact of the gut microbiota on SSI in CD.

METHODS

We used the multicentric REMIND prospective cohort to identify all patients who experienced SSI after ileocolonic resection for CD, defined as any postoperative local septic complication within 90 days after surgery: wound abscess, intra-abdominal collection, anastomotic leakage or enterocutaneous fistula. The mucosa-associated microbiota of the ileal resection specimen was analysed by 16S gene sequencing in 149 patients. The variable selection and prediction were performed with random forests [R package VSURF] on clinical and microbiotal data. The criterion of performance that we considered was the area under the Receiver Operating Characteristic [ROC] curve [AUC].

RESULTS

SSI occurred in 24 patients [16.1%], including 15 patients [10.1%] with major morbidity. There were no significant differences between patients with or without SSI regarding alpha and beta diversity. The top selected variables for the prediction of SSI were all microbiota-related. The maximum AUC [0.796] was obtained with a model including 14 genera, but an AUC of 0.78 had already been obtained with a model including only six genera [Hungatella, Epulopiscium, Fusobacterium, Ruminococcaceae_ucg_009, Actinomyces and Ralstonia].

CONCLUSION

The gut microbiota has the potential to predict SSI after ileocolonic resection for CD. It might play a role in this frequent postoperative complication.

The molecular pathogenesis of triptolide-induced hepatotoxicity

Triptolide (TP) is the major pharmacologically active ingredient and toxic component of Tripterygium wilfordii Hook. f. However, its clinical potential is limited by a narrow therapeutic window and multiple organ toxicity, especially hepatotoxicity. Furthermore, TP-induced hepatotoxicity shows significant inter-individual variability. Over the past few decades, research has been devoted to the study of TP-induced hepatotoxicity and its mechanism. In this review, we summarized the mechanism of TP-induced hepatotoxicity. Studies have demonstrated that TP-induced hepatotoxicity is associated with CYP450s, P-glycoprotein (P-gp), oxidative stress, excessive autophagy, apoptosis, metabolic disorders, immunity, and the gut microbiota. These new findings provide a comprehensive understanding of TP-induced hepatotoxicity and detoxification.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

Sulfated Cyclocarya paliurus polysaccharides improve immune function of immunosuppressed mice by modulating intestinal microbiota

The study was aimed to investigate the effect of Cyclocarya paliurus polysaccharides (CPP) and the sulfation derivative (S-CPP) on modulate intestinal mucosal immunity and intestinal microbiota in cyclophosphamide-induced mice. The results showed that CPP and S-CPP effectively alleviated intestinal villi injury, enhanced the contents of interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) in small intestinal tissue and serum, and upregulated IL-1β at gene levels, zonula occludens-1 (ZO-1), Occludin and Claudin-1 at gene and protein levels, thereby promoting the repair of intestinal mechanical barrier and enhancing intestinal mucosal immunity. Moreover, the beneficial modulation of CPP and S-CPP on the overall structure of intestinal microbiota was revealed by performing 16S ribosomal RNA (16S rRNA) sequencing. Sulfated modification could improve the protection of CPP on the intestinal barrier and the regulation of systemic immunity. S-CPP had a stronger potential to reduce the damage of cyclophosphamide (Cy) on immunity and intestinal microbiota.

NNAN: Nearest Neighbor Attention Network to Predict Drug–Microbe Associations

Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug–microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming. Thus, predicting possible DMAs in computer technology is an essential topic. Inspired by other issues addressed by deep learning, we designed a deep learning-based model named Nearest Neighbor Attention Network (NNAN). The proposed model consists of four components, namely, a similarity network constructor, a nearest-neighbor aggregator, a feature attention block, and a predictor. In brief, the similarity block contains a microbe similarity network and a drug similarity network. The nearest-neighbor aggregator generates the embedding representations of drug–microbe pairs by integrating drug neighbors and microbe neighbors of each drug–microbe pair in the network. The feature attention block evaluates the importance of each dimension of drug–microbe pair embedding by a set of ordinary multi-layer neural networks. The predictor is an ordinary fully-connected deep neural network that functions as a binary classifier to distinguish potential DMAs among unlabeled drug–microbe pairs. Several experiments on two benchmark databases are performed to evaluate the performance of NNAN. First, the comparison with state-of-the-art baseline approaches demonstrates the superiority of NNAN under cross-validation in terms of predicting performance. Moreover, the interpretability inspection reveals that a drug tends to associate with a microbe if it finds its top-l most similar neighbors that associate with the microbe.

The Intestinal Microbiota May Be a Potential Theranostic Tool for Personalized Medicine

The human intestine is colonized by a huge number of microorganisms from the moment of birth. This set of microorganisms found throughout the human body, is called the microbiota; the microbiome indicates the totality of genes that the microbiota can express, i.e., its genetic heritage. Thus, microbiota participates in and influences the proper functioning of the organism. The microbiota is unique for each person; it differs in the types of microorganisms it contains, the number of each microorganism, and the ratio between them, but mainly it changes over time and under the influence of many factors. Therefore, the correct functioning of the human body depends not only on the expression of its genes but also on the expression of the genes of the microorganisms it coexists with. This fact makes clear the enormous interest of community science in studying the relationship of the human microbiota with human health and the incidence of disease. The microbiota is like a unique personalized “mold” for each person; it differs quantitatively and qualitatively for the microorganisms it contains together with the relationship between them, and it changes over time and under the influence of many factors. We are attempting to modulate the microbial components in the human intestinal microbiota over time to provide positive feedback on the health of the host, from intestinal diseases to cancer. These interventions to modulate the intestinal microbiota as well as to identify the relative microbiome (genetic analysis) can range from dietary (with adjuvant prebiotics or probiotics) to fecal transplantation. This article researches the recent advances in these strategies by exploring their advantages and limitations. Furthermore, we aim to understand the relationship between intestinal dysbiosis and pathologies, through the research of resident microbiota, that would allow the personalization of the therapeutic antibiotic strategy.

Rumen and lower gut microbiomes relationship with feed efficiency and production traits throughout the lactation of Holstein dairy cows

Fermentation of dietary nutrients in ruminants' gastrointestinal (GI) tract is an essential mechanism utilized to meet daily energy requirements. Especially in lactating dairy cows, the GI microbiome plays a pivotal role in the breakdown of indigestible plant polysaccharides and supply most AAs, fatty acids, and gluconeogenic precursors for milk synthesis. Although the contribution of the rumen microbiome to production efficiency in dairy cows has been widely researched over the years, variations throughout the lactation and the lower gut microbiome contribution to these traits remain poorly characterized. Therefore, we investigated throughout lactation the relationship between the rumen and lower gut microbiomes with production efficiency traits in Holstein cows. We found that the microbiome from both locations has temporal stability throughout lactation, yet factors such as feed intake levels played a significant role in shaping microbiome diversity. The composition of the rumen microbiome was dependent on feed intake. In contrast, the lower gut microbiome was less dependent on feed intake and associated with a potentially enhanced ability to digest dietary nutrients. Therefore, milk production traits may be more correlated with microorganisms present in the lower gut than previously expected. The current study's findings advance our understanding of the temporal relationship of the rumen and lower gut microbiomes by enabling a broader overview of the gut microbiome and production efficiency towards more sustainable livestock production.

Gut microbiota induces high platelet response in patients with ST segment elevation myocardial infarction after ticagrelor treatment

Background:

Ticagrelor is a first-line drug for the treatment of acute ST elevation myocardial infarction (STEMI). However, approximately 20% STEMI patients taking ticagrelor exhibited a delayed response and the mechanism was still unclear.

Methods:

To explore the mechanism of the poor response of ticagrelor in post-percutaneous coronary intervention (PCI) patients, we enrolled 65 high platelet reactivity (HPR) patients and 90 controls (normal platelet reactivity [NPR]). Pharmacokinetic assessment result showed that the plasma concentrations of ticagrelor and its metabolism production, AR-C124910XX, were lower in HPR patients than controls. Further single nucloetide polymorphism (SNP) analysis identified that there is no difference in ATP binding cassette subfamily B member 1 (ABCB1) gene expression between the NPR group and the HPR group. Metagenomic and metabolomic analyses of fecal samples showed that HPR patients had higher microbial richness and diversity. Transplantation of the gut microbiota from HPR donors to microbiota-depleted mice obviously decreased plasma concentration of ticagrelor.

Results:

Our findings highlight that gut microbiota dysbiosis may be an important mechanism for the ticagrelor of HPR in patients with STEMI and support that modify gut microbiota is a potential therapeutic option for STEMI.

Conclusions:

Our findings highlight that gut microbiota dysbiosis may be an important mechanism for the ticagrelor of HPR in patients with ST elevation myocardial infarction (STEMI) and support that modify gut microbiota is a potential therapeutic option for STEMI

Funding:

NSFC 82170297 and 82070300 from the National Natural Science Foundation of China.

Determining the Composition of Resident and Transient Members of the Oyster Microbiome

To better understand how complex microbial communities become assembled on eukaryotic hosts, it is essential to disentangle the balance between stochastic and deterministic processes that drive their assembly. Deterministic processes can create consistent patterns of microbiome membership that result in persistent resident communities, while stochastic processes can result in random fluctuation of microbiome members that are transient with regard to their association to the host. We sampled oyster reefs from six different populations across the east coast of the United States. At each site we collected gill tissues for microbial community analysis and additionally collected and shipped live oysters to Northeastern University where they were held in a common garden experiment. We then examined the microbiome shifts in gill tissues weekly for 6 weeks using 16S rRNA gene amplicon sequencing. We found a strong population-specific signal in the microbial community composition of field-sampled oysters. Surprisingly, the oysters sampled during the common garden experiment maintained compositionally distinct gill-associated microbial communities that reflected their wild population of origin, even after rearing them in a common garden for several weeks. This indicates that oyster gill-associated microbiota are predominantly composed of resident microbes specific to host population, rather than being a reflection of their immediate biotic and abiotic surroundings. However, certain bacterial taxa tended to appear more frequently on individuals from different populations than on individuals from the same population, indicating that there is a small portion of the gill microbiome that is transient and is readily exchanged with the environmental pool of microbes. Regardless, the majority of gill-associated microbes were resident members that were specific to each oyster population, suggesting that there are strong deterministic factors that govern a large portion of the gill microbiome. A small portion of the microbial communities, however, was transient and moved among oyster populations, indicating that stochastic assembly also contributes to the oyster gill microbiome. Our results are relevant to the oyster aquaculture industry and oyster conservation efforts because resident members of the oyster microbiome may represent microbes that are important to oyster health and some of these key members vary depending on oyster population.

Gut metagenome associations with extensive digital health data in a volunteer-based Estonian microbiome cohort

Microbiome research is starting to move beyond the exploratory phase towards interventional trials and therefore well-characterized cohorts will be instrumental for generating hypotheses and providing new knowledge. As part of the Estonian Biobank, we established the Estonian Microbiome Cohort which includes stool, oral and plasma samples from 2509 participants and is supplemented with multi-omic measurements, questionnaires, and regular linkages to national electronic health records. Here we analyze stool data from deep metagenomic sequencing together with rich phenotyping, including 71 diseases, 136 medications, 21 dietary questions, 5 medical procedures, and 19 other factors. We identify numerous relationships (n = 3262) with different microbiome features. In this study, we extend the understanding of microbiome-host interactions using electronic health data and show that long-term antibiotic usage, independent from recent administration, has a significant impact on the microbiome composition, partly explaining the common associations between diseases.

An insight into the fecal microbiota composition in Romanian patients with ankylosing spondylitis using high-throughput 16S rRNA gene amplicon sequencing

Introduction. Application of next-generation sequencing technology generated a massive amount of information on the gut microbiome composition used to understand its role in the healthy state and in various diseases. We aimed to provide information on the gut microbiota composition of Romanian subjects diagnosed with ankylosing spondylitis, an immune-mediated arthropathy linked to a genetic predisposition and gut dysbiosis.

Methods. Stool samples collected from 25 patients with ankylosing spondylitis and 16 healthy controls were investigated using high-throughput DNA sequencing of 16S rRNA amplicons from seven different hypervariable regions and Ion Torrent PGM instrument. Microbial composition of metagenomic data was analyzed with QIIME software and differential abundance analysis of taxa encompassed linear discriminant analysis effect size (LEfSe).

Results. Overall, 14 phyla, 114 families, 114 genera, and 275 species were identified across the 41 samples, the aggregated data revealing as most abundant the phyla Bacteroidetes, Firmicutes, and Proteobacteria, the families Bacteroidaceae, Prevotellaceae, and Ruminococcaceae, the genera Bacteroides, Prevotella, and Faecalibacterium, and Prevotella copri species. Using various cutoffs for abundance and prevalence, core taxonomic members were identified which in general were shared between the patients and controls. However, evidence was gained that the diversity in the microbiomes from the former cohort was lower than for controls and that certain taxa had significantly different abundance between the two groups.

Conclusion. This study allowed an informative high-throughput 16S rRNA profiling of the gut microbiota needed to identify microbiome signatures of risk in the autochthonous population with AS.

Redrawing therapeutic boundaries: microbiota and cancer

The unexpected roles of the microbiota in cancer challenge explanations of carcinogenesis that focus on tumor-intrinsic properties. Most tumors contain bacteria and viruses, and the host's proximal and distal microbiota influence both cancer incidence and therapeutic responsiveness. Continuing the history of cancer-microbe research, these findings raise a key question: to what extent is the microbiota relevant for clinical oncology? We approach this by critically evaluating three issues: how the microbiota provides a predictive biomarker of cancer growth and therapeutic responsiveness, the microbiota's causal role(s) in cancer development, and how therapeutic manipulations of the microbiota improve patient outcomes in cancer. Clarifying the conceptual and empirical aspects of the cancer-associated microbiota can orient future research and guide its implementation in clinical oncology.

Metagenomics studies for the diagnosis and treatment of prostate cancer

AIM

Mutation occurs in the prostate cell genes, leading to abnormal prostate proliferation and ultimately cancer. Prostate cancer (PC) is one of the most common cancers amongst men, and its prevalence worldwide increases relative to men's age. About 16% of the world's cancers are the result of microbes in the human body. Impaired population balance of symbiosis microbes in the human reproductive system is linked to PC development.

DISCUSSION

With the advent of metagenomics science, the genome sequence of the microbiota of the human body has been unveiled. Therefore, it is now possible to identify a higher range of microbiome changes in PC tissue via the Next Generation Technique, which will have positive consequences in personalized medicine. In this review, we intend to question the role of metagenomics studies in the diagnosis and treatment of PC.

CONCLUSION

The microbial imbalance in the men's genital tract might have an effect on prostate health. Based on next-generation sequencing-generated data, Proteobacteria, Firmicutes, Actinobacteria, and Bacteriodetes are the nine frequent phyla detected in a PC sample, which might be involved in inducing mutation in the prostate cells that cause cancer.

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations

MOTIVATION

Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations.

RESULTS

In this article, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75-95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.

AVAILABILITY AND IMPLEMENTATION

Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Effect of Fluoride in Drinking Water on Fecal Microbial Community in Rats

Intestinal nutrition has a close association with the onset and development of fluorosis. Intestinal microbes play a major role in intestinal nutrition. However, the effect of fluoride on intestinal microbes is still not fully understood. This study aimed to evaluate the dose-response of fluoride on fecal microbes as well as the link between fluorosis and fecal microbes. The results showed that fluoride did not significantly alter the diversity of fecal microbiota, but richness estimators (ACE and Chao) increased first, and then decreased with the increase of water fluoride. At the genus level, 150 mg/L fluoride significantly reduced the abundances of Roseburia and Clostridium sensu stricto, and 100 mg/L and 150 mg/L fluoride obviously increased the abundances of Unclassified Ruminococcaceaes and Unclassified Bdellovibrionales, respectively. The correlation analysis showed fluoride exposure had a negative association with Roseburia and Turicibacter and was positively associated with Pelagibacterium, Unclassified Ruminococcaceae, and Unclassified Bdellovibrionales. Dental fluorosis was negatively associated with Clostridium sensu stricto, Roseburia, Turicibacter, and Paenalcaligenes and had a positive association with Pelagibacterium, Unclassified Ruminococcaceae, and Unclassified Bdellovibrionales. In conclusion, this study firstly reports fluoride in drinking water has a remarkable biphasic effect on fecal microbiota in rats, and some bacteria are significantly associated with fluoride exposure and dental fluorosis. These results indicate the gut microbiota may play an important role in fluorosis, and some bacteria are likely to be developed as biomarkers for fluorosis.

The promise of the gut microbiome as part of individualized treatment strategies

Variability in disease presentation, progression and treatment response has been a central challenge in medicine. Although variability in host factors and genetics are important, it has become evident that the gut microbiome, with its vast genetic and metabolic diversity, must be considered in moving towards individualized treatment. In this Review, we discuss six broad disease groups: infectious disease, cancer, metabolic disease, cardiovascular disease, autoimmune or inflammatory disease, and allergic and atopic diseases. We highlight current knowledge on the gut microbiome in disease pathogenesis and prognosis, efficacy, and treatment-related adverse events and its promise for stratifying existing treatments and as a source of novel therapies. The Review is not meant to be comprehensive for each disease state but rather highlights the potential implications of the microbiome as a tool to individualize treatment strategies in clinical practice. Although early, the outlook is optimistic but challenges need to be overcome before clinical implementation, including improved understanding of underlying mechanisms, longitudinal studies with multiple data layers reflecting gut microbiome and host response, standardized approaches to testing and reporting, and validation in larger cohorts. Given progress in the microbiome field with concurrent basic and clinical studies, the microbiome will likely become an integral part of clinical care within the next decade.

Microbiome therapeutics: exploring the present scenario and challenges

Human gut-microbiome explorations have enriched our understanding of microbial colonization, maturation, and dysbiosis in health-and-disease subsets. The enormous metabolic potential of gut microbes and their role in the maintenance of human health is emerging, with new avenues to use them as therapeutic agents to overcome human disorders. Microbiome therapeutics are aimed at engineering the gut microbiome using additive, subtractive, or modulatory therapy with an application of native or engineered microbes, antibiotics, bacteriophages, and bacteriocins. This approach could overcome the limitation of conventional therapeutics by providing personalized, harmonized, reliable, and sustainable treatment. Its huge economic potential has been shown in the global therapeutics market. Despite the therapeutic and economical potential, microbiome therapeutics is still in the developing stage and is facing various technical and administrative issues that require research attention. This review aims to address the current knowledge and landscape of microbiome therapeutics, provides an overview of existing health-and-disease applications, and discusses the potential future directions of microbiome modulations.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

Interactions between fecal gut microbiome, enteric pathogens, and energy regulating hormones among acutely malnourished rural Gambian children

BACKGROUND

The specific roles that gut microbiota, known pathogens, and host energy-regulating hormones play in the pathogenesis of non-edematous severe acute malnutrition (marasmus SAM) and moderate acute malnutrition (MAM) during outpatient nutritional rehabilitation are yet to be explored.

METHODS

We applied an ensemble of sample-specific (intra- and inter-modality) association networks to gain deeper insights into the pathogenesis of acute malnutrition and its severity among children under 5 years of age in rural Gambia, where marasmus SAM is most prevalent.

FINDINGS

Children with marasmus SAM have distinct microbiome characteristics and biologically-relevant multimodal biomarkers not observed among children with moderate acute malnutrition. Marasmus SAM was characterized by lower microbial richness and biomass, significant enrichments in Enterobacteriaceae, altered interactions between specific Enterobacteriaceae and key energy regulating hormones and their receptors.

INTERPRETATION

Our findings suggest that marasmus SAM is characterized by the collapse of a complex system with nested interactions and key associations between the gut microbiome, enteric pathogens, and energy regulating hormones.  Further exploration of these systems will help inform innovative preventive and therapeutic interventions.

FUNDING

The work was supported by the UK Medical Research Council (MRC; MC-A760-5QX00) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement; Bill and Melinda Gates Foundation (OPP 1066932) and the National Institute of Medical Research (NIMR), UK. This network analysis was supported by NIH U54GH009824 [CLD] and NSF OCE-1558453 [CLD].

Compositional variation of the human fecal microbiome in relation to azo-reducing activity: a pilot study

Background

Through an arsenal of microbial enzymes, the gut microbiota considerably contributes to human metabolic processes, affecting nutrients, drugs, and environmental poisons. Azoreductases are a predominant group of microbiota-derived enzymes involved in xenobiotic metabolism and drug activation, but little is known about how compositional changes in the gut microbiota correlate with its azo-reducing activity.

Results

To this end, we used high-throughput 16S rRNA amplicon sequencing, with Illumina MiSeq, to determine the microbial community composition of stool samples from 16 adults with different azo-reducing activity. High azo-reducing activity positively correlated with the relative abundance of phylum Firmicutes (especially genera Streptococcus and Coprococcus) but negatively with phylum Bacteroidetes (especially genus Bacteroides). Typical variations in the Firmicutes-to-Bacteroidetes and Prevotella-to-Bacteroides ratios were observed among samples. Multivariate analysis of the relative abundance of key microbial taxa and other diversity parameters confirmed the Firmicutes proportion as a major variable differentiating high and non-azo-reducers, while Bacteroidetes relative abundance was correlated with azo-reduction, sex, and BMI.

Conclusions

This pilot study showed that stool samples with higher azo-reducing activity were enriched in Firmicutes but with relatively fewer Bacteroidetes. More samples and studies from different geographical areas are needed to bolster this conclusion. Better characterization of different azoreductase-producing gut microbes will increase our knowledge about the fate and differential human responses to azodye-containing drugs or orally consumed chemicals, thus contributing to efforts towards implementing microbiome testing in precision medicine and toxicology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13099-021-00454-0.