Dysbiosis of the salivary microbiota in pediatric-onset primary sclerosing cholangitis and its potential as a biomarker. Sci Rep. 2018;8:5480. [PMID: 29615776 PMCID: PMC5882660 DOI: 10.1038/s41598-018-23870-w]

Salivary microbiome in kidney diseases: A narrative review

Many research has been conducted since the microbiota's discovery that have focused on the role it plays in health and disease. Microbiota can be divided into categories like intestinal, oral, respiratory, and skin microbiota based on the specific localized areas. To maintain homeostasis and control immunological response, the microbial populations live in symbiosis with the host. On the other hand, dysbiosis of the microbiota can cause diseases including kidney diseases and the deregulation of body functioning. We discuss the current understanding of how various kidney diseases are caused by the salivary microbiome (SM) in this overview. First, we review the studies on the salivary microbiota in diverse clinical situations. The importance of the SM in diabetic kidney disease, chronic kidney disease, membranous nephropathy, and IgA nephropathy is next highlighted. We conclude that the characteristics of the SM of patients with various kidney diseases have revealed the potential of salivary microbial markers as noninvasive tool for the detection of various kidney diseases.

Therapeutic modulation of the liver immune microenvironment

Inflammation is a hallmark of progressive liver diseases such as chronic viral or immune-mediated hepatitis, alcohol-associated liver disease, and NAFLD. Preclinical and clinical studies have provided robust evidence that cytokines and related cellular stress sensors in innate and adaptive immunity orchestrate hepatic disease processes. Unresolved inflammation and liver injury result in hepatic scarring, fibrosis, and cirrhosis, which may culminate in HCC. Liver diseases are accompanied by gut dysbiosis and a bloom of pathobionts, fueling hepatic inflammation. Anti-inflammatory strategies are extensively used to treat human immune-mediated conditions beyond the liver, while evidence for immunomodulatory therapies and cell therapy-based strategies in liver diseases is only emerging. The development and establishment of novel immunomodulatory therapies for chronic liver diseases has been dampened by several clinical challenges, such as invasive monitoring of therapeutic efficacy with liver biopsy in clinical trials and risk of DILI in several studies. Such aspects prevented advancements of novel medical therapies for chronic inflammatory liver diseases. New concepts modulating the liver immune environment are studied and eagerly awaited to improve the management of chronic liver diseases in the future.

Current and emerging biomarkers for ulcerative colitis

INTRODUCTION

Ulcerative colitis (UC) is a chronic illness requiring lifelong management that could be enhanced by personalizing care using biomarkers.

AREAS COVERED

The main biomarker discovery modalities are reviewed, highlighting recent results across the spectrum of applications, including diagnostics (serum anti-αvβ6 antibodies achieving an area under the curve [AUC] = 0.99; serum oncostatin M AUC = 0.94), disease activity assessment (fecal calprotectin and serum trefoil factor 3: AUC > 0.90), prognostication of the need for treatment escalation (whole blood transcriptomic panels and CLEC5A/CDH2 ratio: AUC > 0.90), prediction of treatment response, and early identification of patients with subclinical disease. The use of established biomarkers is discussed, along with new evidence regarding autoantibodies, proteins, proteomic panels, transcriptomic signatures, deoxyribonucleic acid methylation patterns, and UC-specific glycomic and metabolic disturbances.

EXPERT OPINION

Novel biomarkers will pave the way for optimized UC care. However, validation, simplification, and direct clinical translation of complex models may prove challenging. Currently, few candidates exist to assess key characteristics, such as UC susceptibility, histological disease activity, drug response, and long-term disease behavior. Further research will likely not only reveal new tools to tackle these issues but also contribute to understanding UC pathogenesis mechanisms.

CRP in Outpatients with Inflammatory Bowel Disease Is Linked to the Blood Microbiota

The circulation is a closed system that has been assumed to be free from bacteria, but evidence for the existence of a low-density blood microbiota is accumulating. The present study aimed to map the blood microbiota of outpatients with Crohn's disease (CD) or with ulcerative colitis (UC) by 16S metagenomics. A diverse microbiota was observed in the blood samples. Regardless of the type of disease, the alpha diversity of the microbiota was positively associated with C-reactive protein (CRP). The blood microbiota had a surprisingly high proportion of Proteobacteria in comparison with human oral and colonic microbiotas. There was no clear difference in the overall pattern of the microbiota between CD and UC. A non-template control (NTC) was included in the whole process to control for the potential contamination from the environment and reagents. Certain bacterial taxa were concomitantly detected in both blood samples and NTC. However, Acinetobacter, Lactobacillus, Thermicanus and Paracoccus were found in blood from both CD and UC patients but not in NTC, indicating the existence of a specific blood-borne microbiota in the patients. Achromobacter dominated in all blood samples, but a minor amount was also found in NTC. Micrococcaceae was significantly enriched in CD, but it was also detected in high abundance in NTC. Whether the composition of the blood microbiota could be a marker of a particular phenotype in inflammatory bowel disease (IBD) or whether the blood microbiota could be used for diagnostic or therapeutic purposes deserves further attention.

Role of microbiome in autoimmune liver diseases

The microbiome plays a crucial role in integrating environmental influences into host physiology, potentially linking it to autoimmune liver diseases, such as autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis. All autoimmune liver diseases are associated with reduced diversity of the gut microbiome and altered abundance of certain bacteria. However, the relationship between the microbiome and liver diseases is bidirectional and varies over the course of the disease. This makes it challenging to dissect whether such changes in the microbiome are initiating or driving factors in autoimmune liver diseases, secondary consequences of disease and/or pharmacological intervention, or alterations that modify the clinical course that patients experience. Potential mechanisms include the presence of pathobionts, disease-modifying microbial metabolites, and more nonspecific reduced gut barrier function, and it is highly likely that the effect of these change during the progression of the disease. Recurrent disease after liver transplantation is a major clinical challenge and a common denominator in these conditions, which could also represent a window to disease mechanisms of the gut-liver axis. Herein, we propose future research priorities, which should involve clinical trials, extensive molecular phenotyping at high resolution, and experimental studies in model systems. Overall, autoimmune liver diseases are characterized by an altered microbiome, and interventions targeting these changes hold promise for improving clinical care based on the emerging field of microbiota medicine.

The oral microbiome in autoimmune diseases: friend or foe?

The human body is colonized by abundant and diverse microorganisms, collectively known as the microbiome. The oral cavity has more than 700 species of bacteria and consists of unique microbiome niches on mucosal surfaces, on tooth hard tissue, and in saliva. The homeostatic balance between the oral microbiota and the immune system plays an indispensable role in maintaining the well-being and health status of the human host. Growing evidence has demonstrated that oral microbiota dysbiosis is actively involved in regulating the initiation and progression of an array of autoimmune diseases.Oral microbiota dysbiosis is driven by multiple factors, such as host genetic factors, dietary habits, stress, smoking, administration of antibiotics, tissue injury and infection. The dysregulation in the oral microbiome plays a crucial role in triggering and promoting autoimmune diseases via several mechanisms, including microbial translocation, molecular mimicry, autoantigen overproduction, and amplification of autoimmune responses by cytokines. Good oral hygiene behaviors, low carbohydrate diets, healthy lifestyles, usage of prebiotics, probiotics or synbiotics, oral microbiota transplantation and nanomedicine-based therapeutics are promising avenues for maintaining a balanced oral microbiome and treating oral microbiota-mediated autoimmune diseases. Thus, a comprehensive understanding of the relationship between oral microbiota dysbiosis and autoimmune diseases is critical for providing novel insights into the development of oral microbiota-based therapeutic approaches for combating these refractory diseases.

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

The microbiota and the gut-liver axis in primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) offers unique opportunities to explore the gut-liver axis owing to the close association between liver disease and colonic inflammation. It is well established that the gut microbiota in people with PSC differs from that of healthy individuals, but details of the microbial factors that demarcate PSC from inflammatory bowel disease (IBD) without PSC are poorly understood. In this Review, we aim to provide an overview of the latest literature on the gut microbiome in PSC and PSC with IBD, critically examining hypotheses on how microorganisms could contribute to the pathogenesis of PSC. A particular emphasis will be put on pathogenic features of the gut microbiota that might explain the occurrence of bile duct inflammation and liver disease in the context of IBD, and we postulate the potential existence of a specific yet unknown factor related to the gut-liver axis as causative in PSC. Available data are scrutinized in the perspective of therapeutic approaches related to the gut-liver axis.

Implications of gut microbiota in autoimmune liver diseases

Autoimmune liver diseases (AILD) are a group of immune-mediated liver inflammatory diseases with three major forms including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Interaction of both genetic and environmental factors leads to the breakdown of self-tolerance, hence resulting in hyper-responsive of autoantibodies and aggressive autoreactive immune cells. Genetic studies have identified dozens of risk loci associated with initiation and development of AILD. However, the role of exogenous factors remains unclear. Recently, both infectious and inflammatory diseases have been associated with microbiota, which colonizes multiple mucosal surfaces and participates in human physiological process and function in immune system, particularly influencing liver, and biliary system via gut-liver axis. Emerging evidence on the role of gut microbiota has expanded our knowledge of AILD in both pathogenesis and potential therapeutic targets, along with putative diagnosis biomarkers. Herein we review the relationship between host and gut microbiota, discuss their potential roles in disease onset and progression, and summarize the compositional and functional alterations of the microbiota in AILD. We also highlighted the microbiota-based therapeutics such as antibiotics and fecal microbiota transplantation (FMT).

Salivary Diagnostics in Pediatrics and the Status of Saliva-Based Biosensors

Salivary biomarkers are increasingly being used as an alternative to diagnose and monitor the progression of various diseases due to their ease of use, on site application, non-invasiveness, and most likely improved patient compliance. Here, we highlight the role of salivary biosensors in the general population, followed by the application of saliva as a diagnostic tool in the pediatric population. We searched the literature for pediatric applications of salivary biomarkers, more specifically, in children from 0 to 18 years old. The use of those biomarkers spans autoimmune, developmental disorders, oncology, neuropsychiatry, respiratory illnesses, gastrointestinal disorders, and oral diseases. Four major applications of salivary proteins as biomarkers are: (1) dental health (caries, stress from orthodontic appliances, and gingivitis); (2) gastrointestinal conditions (eosinophilic esophagitis, acid reflux, appendicitis); (3) metabolic conditions (obesity, diabetes); and (4) respiratory conditions (asthma, allergic rhinitis, small airway inflammation, pneumonia). Genomics, metabolomics, microbiomics, proteomics, and transcriptomics, are various other classifications for biosensing based on the type of biomarkers used and reviewed here. Lastly, we describe the recent advances in pediatric biosensing applications using saliva. This work guides scientists in fabricating saliva-based biosensors by comprehensively overviewing the potential markers and techniques that can be employed.

Dysbiotic human oral microbiota alters systemic metabolism via modulation of gut microbiota in germ-free mice

Background

The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients.

Aim

We explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects.

Methods

The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Gut microbial communities, hepatic gene expression profiles, and serum metabolites were analyzed.

Results

The gut microbial composition was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of lipid and glucose metabolism-related genes. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with characteristic gut microbial taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice.

Conclusion

The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions

Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.

Salivary microbiome in children with Down syndrome: a case-control study

BACKGROUND

Down syndrome (DS), a most frequently occurring genetic disorder, is associated with oral morphological abnormalities and higher incidence rates of oral diseases. Recent studies have analyzed the oral microbiome to elucidate their relationships with oral diseases and general health; however, reports on the oral microbiome in individuals with DS are scarce. This study aimed to characterize the oral microbiome in children with DS.

METHODS

A total of 54 children aged 1-13 years were enrolled in this case-control study. Of these children, 27 had DS (Case: DS group) and 27 were age-matched healthy children (Control: ND group). Saliva in the oral cavity was collected with a swab, cultured, and tested for cariogenic and periodontopathic bacteria by quantitative polymerase chain reaction (qPCR) detection, and the salivary microbiome was analyzed using next-generation sequencing. The student's t-test, Fisher's exact test, Mann-Whitney U test, and permutational multivariate analysis of variance were used for statistical analysis.

RESULTS

Results of culture and qPCR detection tests for cariogenic and periodontopathic bacteria showed no significant differences in the detected bacteria between the DS and ND groups, with the exception of a significantly higher detection rate of Candida albicans in children with DS with mixed dentition. A comparison of the salivary microbiomes by 16S sequencing showed no significant difference in α diversity; however, it showed a significant difference in β diversity. Children with DS had a higher relative abundance of Corynebacterium and Cardiobacterium, and lower relative abundance of TM7.

CONCLUSIONS

This study provided basic data on the salivary microbiome of children with DS and showed the microbiological markers peculiar to children with DS. However, further research to identify the relationship with oral diseases is warranted.

Salivary Microbiota Associated with Peripheral Microvascular Endothelial Dysfunction

AIMS

Oral health is associated with atherosclerotic cardiovascular disease (ACVD). We previously identified the salivary microbiota characteristics of patients with ACVD. However, whether salivary microbiota is characteristic under impaired vascular endothelial function before ACVD onset remains unclear. Therefore, we aimed to evaluate the characteristics of salivary microbiota associated with peripheral microvascular endothelial dysfunction.

METHODS

We collected saliva samples from 172 community-dwelling elderly individuals without a history of ACVD and performed 16S rRNA metagenomic analysis. We assessed the peripheral microvascular endothelial function using reactive hyperemia index (RHI) and compared the salivary microbiota in the groups with normal (RHI ≥ 2.10), borderline, and abnormal (RHI ＜1.67) peripheral endothelial function. Furthermore, we applied machine learning techniques to evaluate whether salivary microbiota could discriminate between individuals with normal and abnormal endothelial function.

RESULTS

The number of operational taxonomic units (OTUs) was higher in the abnormal group than in the normal group (p=0.037), and differences were found in the overall salivary microbiota structure (unweighted UniFrac distances, p=0.038). The linear discriminant analysis (LDA) effect size (LEfSe) algorithm revealed several significantly differentially abundant bacterial genera between the two groups. An Extra Trees classifier model was built to discriminate between groups with normal and abnormal vascular endothelial function based on the microbial composition at the genus level (AUC=0.810).

CONCLUSIONS

The salivary microbiota in individuals with endothelial dysfunction was distinct from that in individuals with normal endothelial function, indicating that the salivary microbiota may be related to endothelial function.

Antimicrobial strategy for targeted elimination of different microbes, including bacterial, fungal and viral pathogens

The continuous emergence of microbial pathogens for which there are no effective antimicrobials threatens global health, necessitating novel antimicrobial approaches. Here, we present a targeted antimicrobial strategy that can be applied to various microbial pathogens. A photoimmuno-conjugate composed of an antibody against the target pathogen and a photoplastic phthalocyanine-derivative probe that generates photo-induced mechanical stress was developed based on photoimmuno-technology. This strategy, named as photoimmuno-antimicrobial strategy (PIAS), eliminates targeted pathogens, regardless of the target species or drug-resistance status. Specifically, PIAS acts on a broad range of microbes, including the bacterial pathogen Staphylococcus aureus, fungal pathogen Candida albicans, including their drug-resistant strains, and viral pathogen SARS-CoV-2, the causative agent of COVID-19. Furthermore, PIAS protects mice from fatal infections without damaging the non-targeted host microbiota and tissues. This study may contribute to the development of next-generation anti-infective therapies.

Recent discoveries in microbiota dysbiosis, cholangiocytic factors, and models for studying the pathogenesis of primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is a cholangiopathy caused by genetic and microenvironmental changes, such as bile homeostasis disorders and microbiota dysbiosis. Therapeutic options are limited, and proven surveillance strategies are currently lacking. Clinically, PSC presents as alternating strictures and dilatations of biliary ducts, resulting in the typical “beaded” appearance seen on cholangiography. The pathogenesis of PSC is still unclear, but cholangiocytes play an essential role in disease development, wherein a reactive phenotype is caused by the secretion of neuroendocrine factors. The liver–gut axis is implicated in the pathogenesis of PSC owing to the dysbiosis of microbiota, but the underlying mechanism is still poorly understood. Alterations in cholangiocyte responses and related signalling pathways during PSC progression were elucidated by recent research, providing novel therapeutic targets. In this review, we summarise the currently known underlying mechanisms of PSC pathogenesis caused by the dysbiosis of microbiota and newly reported information regarding cholangiocytes in PSC. We also summarise recently reported in vitro and in vivo models for studying the pathogenesis of PSC.

Profiling of the Conjunctival Bacterial Microbiota Reveals the Feasibility of Utilizing a Microbiome-Based Machine Learning Model to Differentially Diagnose Microbial Keratitis and the Core Components of the Conjunctival Bacterial Interaction Network

Both healthy and diseased human ocular surfaces possess their own microbiota. If allowed, opportunistic pathogens within the ocular microbiota may cause microbial keratitis (MK). However, the nonpathogenic component of the ocular microbiota has been proven to undermine the performance of culture, the gold standard of the etiological diagnosis for MK. As the conjunctival bacterial microbiota generates unique alterations with various oculopathies, this study aimed to evaluate the feasibility of distinguishing MK using machine learning based on the characteristics of the conjunctival bacterial microbiome associated with various types of MK. This study also aimed to reveal which bacterial genera constitute the core of the interaction network of the conjunctival bacterial microbiome. Conjunctival swabs collected from the diseased eyes of MK patients and the randomly chosen normal eyes of healthy volunteers were subjected for high-throughput 16S rDNA sequencing. The relative content of each bacterial genus and the composition of bacterial gene functions in every sample were used to establish identification models with the random forest algorithm. Tenfold cross validation was adopted. Accuracy was 96.25% using the bacterial microbiota structure and 93.75% using the bacterial gene functional composition. Therefore, machine learning with the conjunctival bacterial microbiome characteristics might be used for differentiation of MKs as a noninvasive supplementary approach. In addition, this study found that Actinobacteria, Lactobacillus, Clostridium, Helicobacter, and Sphingomonas constitute the core of the interaction network of the conjunctival bacterial microbiome.

Effect of storage temperature and flash-freezing on salivary microbiota profiles based on 16S rRNA-targeted sequencing

The sample storage environment affects gut microbial profiles as assessed using 16S rRNA sequencing. However, the influence of storage condition on human salivary microbial profiles has not been well characterized. Here, we performed an observational study to assess the robustness of microbiota profiles in three different storage environments (-80°C after flash-freezing, -80°C, and -15°C; all for 14 days) compared to immediate DNA extraction using the MiSeq Illumina platform. Notably, the 16S rRNA V1-V2 region amplicon sequencing revealed no difference in microbiota profiles between the immediate extraction and each of three storage conditions. An almost perfect correlation was shown between the immediate extraction and the -15°C storage group for relative abundance at the genus and operational taxonomic unit levels. The intraindividual UniFrac distances among storage methods were significantly shorter than those of interindividual distances. None of the amount of extracted DNA, the α-diversity indices, or the relative abundance at the phylum/genus/operational taxonomic unit level differed among storage methods. These findings indicate that a storage temperature of -15°C without flash-freezing may be optimal in terms of cost advantage and simplicity in 16S rRNA sequencing-based salivary microbial research.

Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background, and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants and diabetes, 56 heterogenous MAFLD patients are characterized by generating multiomics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. The dysbiosis in the oral and gut microbiome is explored and the host-microbiome interactions based on global metabolic and inflammatory processes are revealed. These multiomics data are integrated using the biological network and HS's key features are identified using multiomics data. HS is finally predicted using these key features and findings are validated in a follow-up cohort, where 22 subjects with varying degree of HS are characterized.

Mucosal immunity in primary sclerosing cholangitis: from the bowel to bile ducts and back again

PURPOSE OF REVIEW

In this article, we provide a contemporary overview on PSC pathogenesis, with a specific focus on the role of mucosal immunity.

RECENT FINDINGS

The extent of enteric dysbiosis in PSC has been extensively quantified, with evidence of reduced bacterial diversity and enrichment of species capable of driving lymphocyte recruitment from the gut to the liver. Integrative pathway-based analysis and metagenomic sequencing indicate a reduction in butyrate-producing species, near absence of bacteria that activate the nuclear bile acid receptor FXR, and depletion of species that regulate the synthesis of vitamin B6 and branched-chain amino acids. Immunotyping of the cellular inflammatory infiltrate has identified a population of intrahepatic naive T cells, with tendency to acquire a Th17 polarisation state, paralleled by heightened responses to pathogen stimulation. Moreover, the search for antigen specificity has revealed the presence of overlapping nucleotide clonotypes across the gut and liver, highlighting the ability to recognize a common pool of epitopes bearing structural similarities across afflicted sites.

SUMMARY

Understanding the complex mechanisms that underpin mucosal immune responses between the liver and gut will help identify new druggable targets in PSC, centring on gut microbial manipulation, bile acid therapies, and restoration of immune homeostasis.

The role of oral bacteria in inflammatory bowel disease

Over the past two decades, the importance of the microbiota in health and disease has become evident. Pathological changes to the oral bacterial microbiota, such as those occurring during periodontal disease, are associated with multiple inflammatory conditions, including inflammatory bowel disease. However, the degree to which this association is a consequence of elevated oral inflammation or because oral bacteria can directly drive inflammation at distal sites remains under debate. In this Perspective, we propose that in inflammatory bowel disease, oral disease-associated bacteria translocate to the intestine and directly exacerbate disease. We propose a multistage model that involves pathological changes to the microbial and immune compartments of both the oral cavity and intestine. The evidence to support this hypothesis is critically evaluated and the relevance to other diseases in which oral bacteria have been implicated (including colorectal cancer and liver disease) are discussed.

Vaginal microbiome as a tool for prediction of chorioamnionitis in preterm labor: a pilot study

Intra-amniotic infection (IAI) is a major cause of preterm birth with a poor perinatal prognosis. We aimed to determine whether analyzing vaginal microbiota can evaluate the risk of chorioamnionitis (CAM) in preterm labor cases. Vaginal discharge samples were collected from 83 pregnant women admitted for preterm labor. Based on Blanc’s classification, the participants were divided into CAM (stage ≥ II; n = 46) and non-CAM (stage ≤ I; n = 37) groups. The 16S rDNA amplicons (V1–V2) from vaginal samples were sequenced and analyzed. Using a random forest algorithm, the bacterial species associated with CAM were identified, and a predictive CAM (PCAM) scoring method was developed. The α diversity was significantly higher in the CAM than in the non-CAM group (P < 0.001). The area under the curve was 0.849 (95% confidence interval 0.765–0.934) using the PCAM score. Among patients at < 35 weeks of gestation, the PCAM group (n = 22) had a significantly shorter extended gestational period than the non-PCAM group (n = 25; P = 0.022). Multivariate analysis revealed a significant difference in the frequency of developmental disorders in 3-year-old infants (PCAM, 28%, non-PCAM, 4%; P = 0.022). Analyzing vaginal microbiota can evaluate the risk of IAI. Future studies should establish appropriate interventions for IAI high-risk patients to improve perinatal prognosis.

Characterization of salivary microbiota in elderly patients with type 2 diabetes mellitus: a matched case-control study

OBJECTIVE

The importance of oral health in type 2 diabetes mellitus (T2DM) is widely recognized; however, oral microbiota characteristics associated with T2DM in the elderly population are not well-understood. This study was conducted to evaluate the characteristics of the salivary microbiota in elderly Japanese patients with T2DM.

METHODS

Saliva samples were collected from 42 elderly Japanese patients with T2DM and 42 age- and sex-matched subjects without T2DM (control). 16S ribosomal RNA metagenomic analysis and comparative analysis of both groups were performed. Random forest classification by machine learning was performed to discriminate between the salivary microbiota in the two groups.

RESULTS

There were significant differences in the overall salivary microbiota structure between the T2DM and control groups (beta diversity; unweighted UniFrac distances, p = 0.001; weighted UniFrac distances, p = 0.001). The phylum Firmicutes was abundant in patients with T2DM, whereas the phylum Bacteroidetes was abundant in controls. The T2DM prediction model by random forest based on salivary microbiota data was verified with a high predictive potential in five cross-validation tests (area under the curve (AUC) = 0.938 (95% CI, 0.824-1.000)).

CONCLUSION

Characterization revealed that the salivary microbiota profile of the elderly patients with T2DM is significantly distinct from that of the controls.

CLINICAL RELEVANCE

These data indicate the necessity of oral health management based on the characteristics of the salivary microbiota in elderly patients with T2DM. Our findings will contribute to future research on the development of new diagnostic and therapeutic methods for this purpose.

Microbiome Responses to Vancomycin Treatment in a Child With Primary Sclerosing Cholangitis and Ulcerative Colitis

The therapeutic effects of off-label oral vancomycin in pediatric and adult primary sclerosing cholangitis (PSC)-inflammatory bowel disease, more commonly PSC-ulcerative colitis (UC), indicate the translational relevance of disease-associated microbiome findings. This is the first report on longitudinal salivary and fecal microbiome changes in a pediatric PSC-UC patient over the first 90 days of vancomycin therapy. Increase in bacterial diversity and abundance changes in Fusobacterium, Haemophilus, and Neisseria were observed. Our findings highlight the importance of longitudinal microbiome sampling in PSC-UC and serve as a nidus for larger-scale observations toward advancing microbial therapeutics for PSC.

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning

BACKGROUND

Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adverse outcomes and improve the quality of care.

METHODS

Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train machine learning models (XGBoost and ElasticNet) to predict the occurrence of PSI 8 h prior to clinical suspicion. Prediction for PSI was benchmarked against PRISM-III.

RESULTS

Our model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI = [0.82, 0.85]), sensitivity of 0.73 [0.69, 0.74], and positive predictive value (PPV) of 0.36 [0.34, 0.36]. The PRISM-III conversely achieved a lower sensitivity of 0.19 [0.16, 0.22] and PPV of 0.30 [0.26, 0.34] at a cut-off of ≥ 10. The features with the most impact on the PSI prediction were maximum diastolic blood pressure prior to PSI prediction (mean SHAP = 3.4), height (mean SHAP = 3.2), and maximum temperature prior to PSI prediction (mean SHAP = 2.6).

CONCLUSION

A machine learning model using common features in the electronic medical records can predict the onset of serious infections in children with central venous lines at least 8 h prior to when a clinical team drew a blood culture.

Characterization of Salivary Microbiota in Patients with Atherosclerotic Cardiovascular Disease: A Case-Control Study

Aims: Oral bacteria have been reported to be associated with the pathogenesis of atherosclerosis; however, the relationship between the oral microbiota and atherosclerosis remains unclear. The present study aimed to investigate whether or not salivary microbiota of patients with atherosclerotic cardiovascular disease (ACVD) differs from that of subjects without ACVD, and to characterize the salivary microbiota of patients with ACVD.

Methods: This study included 43 patients with ACVD and 86 age- and sex-matched non-ACVD individuals. 16S rRNA metagenomic analysis were performed using DNA isolated from the saliva samples of the participants. To select unique operational taxonomic unit (OTU) sets of ACVD, we conducted the random forest algorithm in machine learning, followed by confirmation via 10-fold cross-validation

Results: There was no difference in richness or evenness between the ACVD and non-ACVD groups (alpha diversity; observed OTU index, p =0.503; Shannon’s index, p =0.478). However, significant differences were found in the overall salivary microbiota structure (beta diversity; unweighted UniFrac distances, p =0.001; weighted UniFrac distances, p =0.001). The Actinobacteria phylum was highly abundant in patients with ACVD, while the Bacteroidetes phylum was less abundant. The random forest classifier identified 43 OTUs as an optimal marker set of ACVD. In a 10-fold cross validation using the validation data, an area under the curve (AUC) of 0.933 (95% CI, 0.855–1.000) was obtained.

Conclusions: The salivary microbiota in patients with ACVD was distinct from that of non-ACVD individuals, indicating that the salivary microbiota may be related to ACVD.

Gut Microbiome of Children and Adolescents With Primary Sclerosing Cholangitis in Association With Ulcerative Colitis

Few studies reported the relation of intestinal microbiome composition and diversity in pediatric patients with primary sclerosing cholangitis (PSC) and ulcerative colitis (UC). In this cross-sectional study, we selected patients younger than 19 years old from the pediatric gastroenterology and hepatology outpatient clinic of a tertiary hospital to describe the intestinal microbiome of pediatric patients with PSC associated or not to UC. Patients were divided in PSC, PSC+UC, and UC diagnosis. A stool sample was collected from each patient (n=30) and from a healthy relative/neighbor (n=23). The microbiome composition was assessed using MiSeq (Illumina) platform. Differences in microbial composition were found between PSC and PSC+UC groups. The relative abundance of Veillonella and Megasphaera genera were increased depending on patients’ age at diagnosis. Veillonella was also increased in patients who were in an active status of the disease. Both genera were positively correlated to total bilirubin and gamma-glutamyl transferase. As a conclusion, the disease, the age and the disease activity status seem to influence the intestinal microbiome, highlighting the difference of intestinal microbiome profile for patients depending on age at diagnosis. We also showed an increase of Veillonella in patients with PSC and PSC+UC, and a positive correlation of dysbiosis and higher gamma-glutamyl transferase and total bilirubin in PSC+UC patients. Our findings are promising in the diagnosis, prognosis, and future therapeutic perspectives for PSC patients.

Rebamipide ameliorates indomethacin-induced small intestinal damage and proton pump inhibitor-induced exacerbation of this damage by modulation of small intestinal microbiota

Non-steroidal anti-inflammatory drugs (NSAIDs) induce small intestinal damage. It has been reported that rebamipide, a mucoprotective drug, exerts a protective effect against NSAID-induced small intestinal damage; however, the underlying mechanism remains unknown. In this study, we investigated the significance of the small intestinal microbiota in the protective effect of rebamipide against indomethacin-induced small intestinal damage in mice. A comprehensive analysis of the 16S rRNA gene sequencing revealed an alteration in the composition of the small intestinal microbiota at the species level, modulated by the administration of rebamipide and omeprazole. The transplantation of the small intestinal microbiota of the mice treated with rebamipide suppressed the indomethacin-induced small intestinal damage. Omeprazole, a proton pump inhibitor, exacerbated the indomethacin-induced small intestinal damage, which was accompanied by the alteration of the small intestinal microbiota. We found that the transplantation of the small intestinal microbiota of the rebamipide-treated mice ameliorated indomethacin-induced small intestinal damage and the omeprazole-induced exacerbation of the damage. These results suggest that rebamipide exerts a protective effect against NSAID-induced small intestinal damage via the modulation of the small intestinal microbiota, and that its ameliorating effect extends also to the exacerbation of NSAID-induced small intestinal damage by proton pump inhibitors.

Comparison of oral microbiome profiles in 18-month-old infants and their parents

The onset and progress of dental caries and periodontal disease is associated with the oral microbiome. Therefore, it is important to understand the factors that influence oral microbiome formation. One of the factors that influence oral microbiome formation is the transmission of oral bacteria from parents. However, it remains unclear when the transmission begins, and the difference in contributions of father and mother. Here, we focused on the oral microbiome of 18-month-old infants, at which age deciduous dentition is formed and the oral microbiome is likely to become stable, with that of their parents. We collected saliva from forty 18-month-old infants and their parents and compared the diversity and composition of the microbiome using next-generation sequencing of 16S rRNA genes. The results showed that microbial diversity in infants was significantly lower than that in parents and composition of microbiome were significantly different between infants and parents. Meanwhile, the microbiome of the infants was more similar to that of their mothers than unrelated adults. The bacteria highly shared between infants and parents included not only commensal bacteria but also disease related bacteria. These results suggested that the oral microbiome of the parents influences that of their children aged < 18 months.

Long-term persistence of gastric dysbiosis after eradication of Helicobacter pylori in patients who underwent endoscopic submucosal dissection for early gastric cancer

BACKGROUND

Gastric microbiome, other than Helicobacter pylori, plays a role in the tumorigenesis of gastric cancer (GC). Patients who undergo endoscopic submucosal dissection for early GC have a high risk of developing metachronous GC even after successful eradication of H. pylori. Thus, we investigated the microbial profiles and associated changes in such patients after the eradication of H. pylori.

METHODS

A total of 19 H. pylori-infected patients with early GC who were or to be treated by endoscopic resection, with paired biopsy samples at pre- and post-eradication therapy, were retrospectively enrolled. Ten H. pylori-negative patients were enrolled as controls. Biopsy samples were analyzed using 16S rRNA sequencing.

RESULTS

H. pylori-positive patients exhibited low richness and evenness of bacteria with the deletion of several genera, including Blautia, Ralstonia, Faecalibacterium, Methylobacterium, and Megamonas. H. pylori eradication partially restored microbial diversity, as assessed during a median follow-up at 13 months after eradication therapy. However, post-eradication patients had less diversity than that in the controls and possessed a lower abundance of the five genera mentioned above. The eradication of H. pylori also altered the bacterial composition, but not to the same extent as that in controls. The microbial communities could be clustered into three separate groups: H. pylori-negative, pre-eradication, and post-eradication.

CONCLUSION

Changes in dysbiosis may persist long after the eradication of H. pylori in patients with a history of GC. Dysbiosis may be involved in the development of both primary and metachronous GC after the eradication of H. pylori in such patients.

Dysbiosis in the Salivary Microbiome Associated with IgA Nephropathy-‍A‍ ‍Japanese Cohort Study

IgA nephropathy is one of the leading causes of chronic kidney disease in Japan. Since the origin and mechanisms by which IgA nephropathy develops currently remain unclear, a confirmed disease diagnosis is currently only possible by highly invasive renal biopsy. With the background of the salivary microbiome as a rich source of biomarkers for systemic diseases, we herein primarily aimed to investigate the salivary microbiome as a tool for the non-invasive diagnosis of IgA nephropathy. In a comparison of salivary microbiome profiles using 16S rRNA amplicon sequencing, significant differences were observed in microbial diversity and richness between IgA nephropathy patients and healthy controls. Furthermore, recent studies reported that patients with IgA nephropathy are more likely to develop inflammatory bowel diseases and that chronic inflammation of the tonsils triggered the recurrence of IgA nephropathy. Therefore, we compared the salivary microbiome of IgA nephropathy patients with chronic tonsillitis and ulcerative colitis patients. By combining the genera selected by the random forest algorithm, we were able to distinguish IgA nephropathy from healthy controls with an area under the curve (AUC) of 0.90, from the ulcerative colitis group with AUC of 0.88, and from the chronic tonsillitis group with AUC of 0.70. Additionally, the genus Neisseria was common among the selected genera that facilitated the separation of the IgA nephropathy group from healthy controls and the chronic tonsillitis group. The present results indicate the potential of the salivary microbiome as a biomarker for the non-invasive diagnosis of IgA nephropathy.

Gut Microbiota Profile in Pediatric Patients With Inflammatory Bowel Disease: A Systematic Review

Background and Aim: Accumulating evidence have implicated gut microbiota alterations in pediatric and adult patients with inflammatory bowel disease (IBD); however, the results of different studies are often inconsistent and even contradictory. It is believed that early changes in new-onset and treatment-naïve pediatric patients are more informative. We performed a systematic review to investigate the gut microbiota profiles in pediatric IBD and identify specific microbiota biomarkers associated with this disorder. Methods: Electronic databases were searched from inception to 31 July 2020 for studies that observed gut microbiota alterations in pediatric patients with IBD. Study quality was assessed using the Newcastle-Ottawa scale. Results: A total of 41 original studies investigating gut microbiota profiles in pediatric patients with IBD were included in this review. Several studies have reported a decrease in α-diversity and an overall difference in β-diversity. Although no specific gut microbiota alterations were consistently reported, a gain in Enterococcus and a significant decrease in Anaerostipes, Blautia, Coprococcus, Faecalibacterium, Roseburia, Ruminococcus, and Lachnospira were found in the majority of the included articles. Moreover, there is insufficient data to show specific microbiota bacteria associated with disease activity, location, and behavior in pediatric IBD. Conclusions: This systematic review identified evidence for differences in the abundance of some bacteria in pediatric patients with IBD when compared to patients without IBD; however, no clear overall conclusion could be drawn from the included studies due to inconsistent results and heterogeneous methodologies. Further studies with large samples that follow more rigorous and standardized methodologies are needed.

Gut microbiota and artificial intelligence approaches: A scoping review

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Commensal Oral Rothia mucilaginosa Produces Enterobactin, a Metal-Chelating Siderophore

Next-generation sequencing studies of saliva and dental plaque from subjects in both healthy and diseased states have identified bacteria belonging to the Rothia genus as ubiquitous members of the oral microbiota. To gain a deeper understanding of molecular mechanisms underlying the chemical ecology of this unexplored group, we applied a genome mining approach that targets functionally important biosynthetic gene clusters (BGCs). All 45 genomes that were mined, representing Rothia mucilaginosa, Rothia dentocariosa, and Rothia aeria, harbored a catechol-siderophore-like BGC. To explore siderophore production further, we grew the previously characterized R. mucilaginosa ATCC 25296 in liquid cultures, amended with glycerol, which led to the identification of the archetype siderophore enterobactin by using tandem liquid chromatography-mass spectrometry (LC-MS/MS), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) spectroscopy. Normally attributed to pathogenic gut bacteria, R. mucilaginosa is the first commensal oral bacterium found to produce enterobactin. Cocultivation studies including R. mucilaginosa or purified enterobactin revealed that enterobactin reduced growth of certain strains of cariogenic Streptococcus mutans and pathogenic strains of Staphylococcus aureus Commensal oral bacteria were either unaffected, reduced in growth, or induced to grow adjacent to enterobactin-producing R. mucilaginosa or the pure compound. Taken together with Rothia's known capacity to ferment a variety of carbohydrates and amino acids, our findings of enterobactin production add an additional level of explanation to R. mucilaginosa's prevalence in the oral cavity. Enterobactin is the strongest Fe(III) binding siderophore known, and its role in oral health requires further investigation.IMPORTANCE The communication language of the human oral microbiota is vastly underexplored. However, a few studies have shown that specialized small molecules encoded by BGCs have critical roles such as in colonization resistance against pathogens and quorum sensing. Here, by using a genome mining approach in combination with compound screening of growth cultures, we identified that the commensal oral community member R. mucilaginosa harbors a catecholate-siderophore BGC, which is responsible for the biosynthesis of enterobactin. The iron-scavenging role of enterobactin is known to have positive effects on the host's iron pool and negative effects on host immune function; however, its role in oral health remains unexplored. R. mucilaginosa was previously identified as an abundant community member in cystic fibrosis, where bacterial iron cycling plays a major role in virulence development. With respect to iron's broad biological importance, iron-chelating enterobactin may explain R. mucilaginosa's colonization success in both health and disease.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Fungi participate in the dysbiosis of gut microbiota in patients with primary sclerosing cholangitis

OBJECTIVE

Patients with primary sclerosing cholangitis (PSC) were previously shown to display a bacterial gut dysbiosis but fungal microbiota has never been examined in these patients. The aim of this study was to assess the fungal gut microbiota in patients with PSC.

DESIGN

We analysed the faecal microbiota of patients with PSC and concomitant IBD (n=27), patients with PSC and no IBD (n=22), patients with IBD and no PSC (n=33) and healthy subjects (n=30). Bacterial and fungal composition of the faecal microbiota was determined using 16S and ITS2 sequencing, respectively.

RESULTS

We found that patients with PSC harboured bacterial dysbiosis characterised by a decreased biodiversity, an altered composition and a decreased correlation network density. These alterations of the microbiota were associated with PSC, independently of IBD status. For the first time, we showed that patients with PSC displayed a fungal gut dysbiosis, characterised by a relative increase in biodiversity and an altered composition. Notably, we observed an increased proportion of Exophiala and a decreased proportion of Saccharomyces cerevisiae. Compared with patients with IBD and healthy subjects, the gut microbiota of patients with PSC exhibited a strong disruption in bacteria-fungi correlation network, suggesting an alteration in the interkingdom crosstalk.

CONCLUSION

This study demonstrates that bacteria and fungi contribute to gut dysbiosis in PSC.

Altered microbiota composition reflects enhanced communication in 15q11-13 CNV mice

Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder. In addition to the core symptoms of ASD, many patients with ASD also show comorbid gut dysbiosis, which may lead to various gastrointestinal (GI) problems. Intriguingly, there is evidence that gut microbiota communicate with the central nervous system to modulate behavioral output through the gut-brain axis. To investigate how the microbiota composition is changed in ASD and to identify which microbes are involved in autistic behaviors, we performed a 16S rRNA gene-based metagenomics analysis in an ASD mouse model. Here, we focused on a model with human 15q11-13 duplication (15q dup), the most frequent chromosomal aberration or copy number variation found in ASD. Species diversity of the microbiome was significantly decreased in 15q dup mice. A combination of antibiotics treatment and behavioral analysis showed that neomycin improved social communication in 15q dup mice. Furthermore, comparison of the microbiota composition of mice treated with different antibiotics enabled us to identify beneficial operational taxonomic units (OTUs) for ultrasonic vocalization.

Gastric acid inhibitor aggravates indomethacin-induced small intestinal injury via reducing Lactobacillus johnsonii

Proton pump inhibitors (PPIs) alter the composition of the intestinal microbiome, exacerbating indomethacin (IND)-induced small intestinal damage. Vonoprazan fumarate inhibits gastric acid secretion using a different mechanism from PPIs. We investigated the effects of both drugs on the intestinal microbiome and IND-induced small intestinal damage. We sought to clarify whether PPI-induced dysbiosis and worsening of the damage were due to a specific drug class effect of PPIs. Rabeprazole administration increased operational taxonomic unit numbers in the small intestines of C57BL/6 J mice, whereas the difference was not significant in the vonoprazan-treated group but exhibited a trend. Permutational multivariate analysis of variance of the unweighted UniFrac distances showed significant differences between vehicle- and vonoprazan- or rabeprazole-treated groups. L. johnsonii was the predominant microbial species, and the population ratio decreased after vonoprazan and rabeprazole administration. The vonoprazan- and rabeprazole-treated groups showed increased IND-induced damage. This high sensitivity to IND-induced damage was evaluated by transplantation with contents from the small intestine of mice treated with either vonoprazan or rabeprazole. Supplementation of L. johnsonii orally in mice treated with rabeprazole and vonoprazan prevented the increase in IND-induced small intestinal damage. In conclusion, both rabeprazole and vonoprazan aggravated NSAID-induced small intestinal injury by reducing the population of L. johnsonii in the small intestine via suppressing gastric acid secretion.

HS-SPME-GC-MS approach for the analysis of volatile salivary metabolites and application in a case study for the indirect assessment of gut microbiota

In this work, a straightforward analytical approach based on headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was developed for the analysis of salivary volatile organic compounds without any prior derivatization step. With a sample volume of 500 μL, optimal conditions were achieved by allowing the sample to equilibrate for 10 min at 50 °C and then extracting the samples for 10 min at the same temperature, using a carboxen/polydimethylsiloxane fibre. The method allowed the simultaneous identification and quantification of 20 compounds in sample headspace, including short-chain fatty acids and their derivatives which are commonly analysed after analyte derivatization. The proof of applicability of the methodology was performed with a case study regarding the analysis of the dynamics of volatile metabolites in saliva of a single subject undergoing 5-day treatment with rifaximin antibiotic. Non-stimulated saliva samples were collected over 3 weeks from a nominally healthy volunteer before, during, and after antibiotic treatment. The variations of some metabolites, known to be produced by the microbiota and by bacteria that are susceptible to antibiotics, suggest that the study of the dynamics of salivary metabolites can be an excellent indirect method for analysing the gut microbiota. This approach is novel from an analytical standpoint, and it encourages further studies combining saliva metabolite profiles and gut microbiota dynamics. Graphical abstract.

A Network Involving Gut Microbiota, Circulating Bile Acids, and Hepatic Metabolism Genes That Protects Against Non-Alcoholic Fatty Liver Disease

SCOPE

Gut microbiota contributes to non-alcoholic fatty liver disease (NAFLD) pathogenesis by multiple mechanisms not yet completely understood. Novel differential features between germ-free mice (GFm) transplanted with protective or non-protective cecal microbiota against NAFLD are investigated.

METHODS AND RESULTS

Gut microbiota composition, plasma, and fecal bile acids (BAs) and liver mRNAs are quantified in GFm recipients from four donor mice differing in NAFLD severity (control diet, high-fat diet [HFD]-responder, HFD-non-responder, and quercetin-supplemented HFD). Transplanted GFm are on control or HFD for 16-weeks. Multivariate analysis shows that GFm colonized with microbiota from HFD-non-responder and quercetin supplemented-HFD donors (protected against NAFLD) clusters together, whereas GFm colonized with microbiota from control and HFD-responder mice (non-protected against NAFLD) establishes another cluster. Protected phenotype is associated with increased gut Desulfovibrio and Oscillospira, reduced gut Bacteroides and Oribacterium, lower primary and higher secondary BAs in plasma and feces, induction of hepatic BA transporters, and repression of hepatic lipogenic and BA synthesis genes.

CONCLUSION

Protective gut microbiota associates with increased specific secondary BAs, which likely inhibit lipogenic pathways and enhance bile flow in the liver. This novel cross-talk between gut and liver, via plasma BAs, that promotes protection against NAFLD may have clinical and nutritional relevance.

The gut microbial influence on cholestatic liver disease

Patients with cholestatic liver diseases like primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) have a different gut microbiome composition than healthy controls. In contrast with PBC, PSC has a strong association with inflammatory bowel disease and is the prototypical disease of the gut-liver axis. Still, there are some distinct overlapping microbial features in the microbiome of patients with PSC and PBC suggesting similarities in cholestatic diseases, although the possible pathogenetic involvement of these shared microbial changes is unknown. Herein, we present an overview of the available data and discuss the relevance for potential disease relevant host-microbiota interactions. In general, the microbiome interacts with the host via the immunobiome (interactions between the host immune system and the gut microbiome), the endobiome (where the gut microbiome contributes to host physiology by producing or metabolizing endogenous molecules) and the xenobiome (gut microbial transformation of exogenous compounds, including nutrients and drugs). Experimental and human observational evidence suggest that the presence and functions of gut microbes are relevant for the severity and progression of cholestatic liver disease. Interestingly, the majority of new drugs that are currently being tested in PBC and PSC in clinical trials act on bile acid homeostasis, where the endobiome is important. In the future, it will be paramount to perform longitudinal studies, through which we can identify new intervention targets, biomarkers or treatment-stratifiers. In this way, gut microbiome-based clinical care and therapy may become relevant in cholestatic liver disease within the foreseeable future.

Treatment of primary sclerosing cholangitis in children

Primary sclerosing cholangitis (PSC) is a rare disease of stricturing and destruction of the biliary tree with a complex genetic and environmental etiology. Most patients have co-occurring inflammatory bowel disease. Children generally present with uncomplicated disease, but undergo a variable progression to end-stage liver disease. Within ten years of diagnosis, 50% of children will develop clinical complications including 30% requiring liver transplantation. Cholangiocarcinoma is a rare but serious complication affecting 1% of children. Ursodeoxycholic acid and oral vancomycin therapy used widely in children as medical therapy, and may be effective in a subset of patients. Gamma glutamyltransferase is a potential surrogate endpoint for disease activity, with improved survival in patients who achieve a normal value. Endoscopic retrograde cholangiopancreatography is a necessary adjunct to medical therapy to evaluate mass lesions or dominant strictures for malignancy, and also to relieve biliary obstruction. Liver transplantation remains the only option for patients who progress to end-stage liver disease. We review special considerations for patients before and after transplant, and in patients with inflammatory bowel disease. There is presently no published treatment algorithm or guideline for the management of children with PSC. We review the evidence for drug efficacy, dosing, duration of therapy, and treatment targets in PSC, and provide a framework for endoscopic and medical management of this complex problem.