Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians

Early Gut Microbiota Profile in Healthy Neonates: Microbiome Analysis of the First-Pass Meconium Using Next-Generation Sequencing Technology

Gut microbiome development during early life has significant long-term effects on health later in life. The first-pass meconium is not sterile, and it is important to know the initial founder of the subsequent gut microbiome. However, there is limited data on the microbiota profile of the first-pass meconium in healthy neonates. To determine the early gut microbiota profile, we analyzed 39 samples of the first-pass meconium from healthy neonates using 16S rRNA sequencing. Our results showed a similar profile of the microbiota composition in the first-pass meconium samples. Pseudomonas was the most abundant genus in most samples. The evenness of the microbial communities in the first-pass meconium was extremely poor, and the average Shannon diversity index was 1.31. An analysis of the relationship between perinatal characteristics and the meconium microbiome revealed that primigravidae babies had a significantly higher Shannon diversity index (p = 0.041), and the Bacteroidales order was a biomarker for the first-pass meconium of these neonates. The Shannon diversity index was not affected by the mode of delivery, maternal intrapartum antibiotic treatment, prolonged rupture of membranes, or birth weight. Our study extends previous research with further characterization of the gut microbiome in very early life.

Microbiome-metabolomics analysis reveals abatement effects of itaconic acid on odorous compound production in Arbor Acre broilers

BACKGROUND

Public complaints concerning odor emissions from intensive livestock and poultry farms continue to grow, as nauseous odorous compounds have adverse impacts on the environment and human health. Itaconic acid is a metabolite from the citric acid cycle of the host and shows volatile odor-reducing effects during animal production operations. However, the specific role of itaconic acid in decreasing intestinal odorous compound production remains unclear. A total of 360 one-day-old chicks were randomly divided into 6 treatment groups: control group (basal diet) and itaconic acid groups (basal diet + 2, 4, 6, 8 and 10 g/kg itaconic acid). The feeding experiment lasted for 42 d.

RESULTS

Dietary itaconic acid supplementation linearly and quadratically decreased (P < 0.05) the cecal concentrations of indole and skatole but did not affect (P > 0.05) those of lactic, acetic, propionic and butyric acids. The cecal microbial shift was significant in response to 6 g/kg itaconic acid supplementation, in that the abundances of Firmicutes, Ruminococcus and Clostridium were increased (P < 0.05), while those of Bacteroidetes, Escherichia-Shigella and Bacteroides were decreased (P < 0.05), indicative of increased microbial richness and diversity. Furthermore, a total of 35 significantly (P < 0.05) modified metabolites were obtained by metabolomic analysis. Itaconic acid decreased (P < 0.05) the levels of nicotinic acid, nicotinamide, glucose-6-phosphate, fumatic acid and malic acid and increased (P < 0.05) 5-methoxytroptomine, dodecanoic acid and stearic acid, which are connected with the glycolytic pathway, citrate acid cycle and tryptophan metabolism. Correlation analysis indicated significant correlations between the altered cecal microbiota and metabolites; Firmicutes, Ruminococcus and Clostridium were shown to be negatively correlated with indole and skatole production, while Bacteroidetes, Escherichia-Shigella and Bacteroides were positively correlated with indole and skatole production.

CONCLUSIONS

Itaconic acid decreased cecal indole and skatole levels and altered the microbiome and metabolome in favor of odorous compound reduction. These findings provide new insight into the role of itaconic acid and expand its application potential in broilers.

An efficient method for high molecular weight bacterial DNA extraction suitable for shotgun metagenomics from skin swabs

The human skin microbiome represents a variety of complex microbial ecosystems that play a key role in host health. Molecular methods to study these communities have been developed but have been largely limited to low-throughput quantification and short amplicon-based sequencing, providing limited functional information about the communities present. Shotgun metagenomic sequencing has emerged as a preferred method for microbiome studies as it provides more comprehensive information about the species/strains present in a niche and the genes they encode. However, the relatively low bacterial biomass of skin, in comparison to other areas such as the gut microbiome, makes obtaining sufficient DNA for shotgun metagenomic sequencing challenging. Here we describe an optimised high-throughput method for extraction of high molecular weight DNA suitable for shotgun metagenomic sequencing. We validated the performance of the extraction method, and analysis pipeline on skin swabs collected from both adults and babies. The pipeline effectively characterised the bacterial skin microbiota with a cost and throughput suitable for larger longitudinal sets of samples. Application of this method will allow greater insights into community compositions and functional capabilities of the skin microbiome.

Maternal Vitamin A deficiency during pregnancy and lactation induced damaged intestinal structure and intestinal flora homeostasis in offspring mice

The small intestine serves as the first channel of dietary Vitamin A (VA) and the unique organ of VA absorption and metabolism. However, there have not been extensive investigations on the exact mechanisms within VA-related changes in intestinal metabolic disorders. This research is designed to analyze whether and how VA affects intestinal metabolic phenotypes. Male C57BL/6 mice after weaning were randomly fed a VA control diet (VAC) or a VA-deficient diet (VAD) during the entire pregnancy and lactation process. After a total of 11 weeks, cohorts of VA deprived were next fed to a VA control diet (VAD-C) for another 8 weeks. The concentration of retinol was measured by a high-performance liquid chromatography system. The 16S gene sequencing was used to evaluate the intestinal microbiota changes. Through the use of histological staining, western blots, quantitative PCR, and enzyme-linked immunosorbent assays, the intestinal morphology, inflammatory factors, and intestinal permeability were all evaluated. Following the decrease of the tissue VA levels, VAD mice show a decrease in tissue VA levels, community differences, and the richness and diversity of intestinal microbiota. VAD diet-driven changes occur in intestinal microbiota, accompanied by a higher mRNA expression of intestinal inflammatory cytokines and an increase in intestinal permeability. As dietary VA is reintroduced into VAD diet-fed mice, the tissue VA levels, inflammatory response, and intestinal homeostasis profiles are all restored, which are similar to those found after the occurrence of VA-controlled changes within intestinal microbiota. VA deficiency caused the imbalance of intestinal metabolic phenotypes through a mechanism involving changes in intestinal microbiota. It is thought that intestinal microbiota metabolic influences represent a new salient and additional mechanism, which can be used as a new method to achieve the onset and treatment of the effect of VAD on intestinal homeostasis impairment.

Gut microbiota in patients with kidney stones: a systematic review and meta-analysis

BACKGROUND

Mounting evidence indicates that the gut microbiome (GMB) plays an essential role in kidney stone (KS) formation. In this study, we conducted a systematic review and meta-analysis to compare the composition of gut microbiota in kidney stone patients and healthy individuals, and further understand the role of gut microbiota in nephrolithiasis.

RESULTS

Six databases were searched to find taxonomy-based comparison studies on the GMB until September 2022. Meta-analyses were performed using RevMan 5.3 to estimate the overall relative abundance of gut microbiota in KS patients and healthy subjects. Eight studies were included with 356 nephrolithiasis patients and 347 healthy subjects. The meta-analysis suggested that KS patients had a higher abundance of Bacteroides (35.11% vs 21.25%, Z = 3.56, P = 0.0004) and Escherichia_Shigella (4.39% vs 1.78%, Z = 3.23, P = 0.001), and a lower abundance of Prevotella_9 (8.41% vs 10.65%, Z = 4.49, P < 0.00001). Qualitative analysis revealed that beta-diversity was different between the two groups (P < 0.05); Ten taxa (Bacteroides, Phascolarctobacterium, Faecalibacterium, Flavobacterium, Akkermansia, Lactobacillus, Escherichia coli, Rhodobacter and Gordonia) helped the detection of kidney stones (P < 0.05); Genes or protein families of the GMB involved in oxalate degradation, glycan synthesis, and energy metabolism were altered in patients (P < 0.05).

CONCLUSIONS

There is a characteristic gut microbiota dysbiosis in kidney stone patients. Individualized therapies like microbial supplementation, probiotic or synbiotic preparations and adjusted diet patterns based on individual gut microbial characteristics of patients may be more effective in preventing stone formation and recurrence.

Human gut microbiome: A primer for the clinician

The human host gets tremendously influenced by a genetically and phenotypically distinct and heterogeneous constellation of microbial species-the human microbiome-the gut being one of the most densely populated and characterized site for these organisms. Microbiome science has advanced rapidly, technically with respect to the analytical methods and biologically with respect to its mechanistic influence in health and disease states. A clinician conducting a microbiome study should be aware of the nuances related to microbiome research, especially with respect to the technical and biological factors that can influence the interpretation of research outcomes. Hence, this review is an attempt to detail these aspects of the human gut microbiome, with emphasis on its determinants in a healthy state.

Epsilon toxin-producing Clostridium perfringens colonize the multiple sclerosis gut microbiome overcoming CNS immune privilege

Multiple sclerosis (MS) is a complex disease of the CNS thought to require an environmental trigger. Gut dysbiosis is common in MS, but specific causative species are unknown. To address this knowledge gap, we used sensitive and quantitative PCR detection to show that people with MS were more likely to harbor and show a greater abundance of epsilon toxin-producing (ETX-producing) strains of C. perfringens within their gut microbiomes compared with individuals who are healthy controls (HCs). Isolates derived from patients with MS produced functional ETX and had a genetic architecture typical of highly conjugative plasmids. In the active immunization model of experimental autoimmune encephalomyelitis (EAE), where pertussis toxin (PTX) is used to overcome CNS immune privilege, ETX can substitute for PTX. In contrast to PTX-induced EAE, where inflammatory demyelination is largely restricted to the spinal cord, ETX-induced EAE caused demyelination in the corpus callosum, thalamus, cerebellum, brainstem, and spinal cord, more akin to the neuroanatomical lesion distribution seen in MS. CNS endothelial cell transcriptional profiles revealed ETX-induced genes that are known to play a role in overcoming CNS immune privilege. Together, these findings suggest that ETX-producing C. perfringens strains are biologically plausible pathogens in MS that trigger inflammatory demyelination in the context of circulating myelin autoreactive lymphocytes.

Lung-gut axis of microbiome alterations following co-exposure to ultrafine carbon black and ozone

BACKGROUND

Microbial dysbiosis is a potential mediator of air pollution-induced adverse outcomes. However, a systemic comparison of the lung and gut microbiome alterations and lung-gut axis following air pollution exposure is scant. In this study, we exposed male C57BL/6J mice to inhaled air, CB (10 mg/m3), O3 (2 ppm) or CB + O3 mixture for 3 h/day for either one day or four consecutive days and were euthanized 24 h post last exposure. The lung and gut microbiome were quantified by 16 s sequencing.

RESULTS

Multiple CB + O3 exposures induced an increase in the lung inflammatory cells (neutrophils, eosinophils and B lymphocytes), reduced absolute bacterial load in the lungs and increased load in the gut. CB + O3 exposure was more potent as it decreased lung microbiome alpha diversity just after a single exposure. CB + O3 co-exposure uniquely increased Clostridiaceae and Prevotellaceae in the lungs. Serum short chain fatty acids (SCFA) (acetate and propionate) were increased significantly only after CB + O3 co-exposure. A significant increase in SCFA producing bacterial families (Ruminococcaceae, Lachnospiraceae, and Eubacterium) were also observed in the gut after multiple exposures. Co-exposure induced significant alterations in the gut derived metabolite receptors/mediator (Gcg, Glp-1r, Cck) mRNA expression. Oxidative stress related mRNA expression in lungs, and oxidant levels in the BALF, serum and gut significantly increased after CB + O3 exposures.

CONCLUSION

Our study confirms distinct gut and lung microbiome alterations after CB + O3 inhalation co-exposure and indicate a potential homeostatic shift in the gut microbiome to counter deleterious impacts of environmental exposures on metabolic system.

Effect of Non-Nutritive Sweeteners on the Gut Microbiota

The human gut microbiota, a complex community of microorganisms living in the digestive tract, consists of more than 1500 species distributed in more than 50 different phyla, with 99% of bacteria coming from about 30-40 species. The colon alone, which contains the largest population of the diverse human microbiota, can harbor up to 100 trillion bacteria. The gut microbiota is essential in maintaining normal gut physiology and health. Therefore, its disruption in humans is often associated with various pathological conditions. Different factors can influence the composition and function of the gut microbiota, including host genetics, age, antibiotic treatments, environment, and diet. The diet has a marked effect, impacting the gut microbiota composition, beneficially or detrimentally, by altering some bacterial species and adjusting the metabolites produced in the gut environment. With the widespread use of non-nutritive sweeteners (NNS) in the diet, recent investigations have focused on their effect on the gut microbiota as a mediator of the potential impact generated by gastrointestinal-related disturbances, such as insulin resistance, obesity, and inflammation. We summarized the results from pre-clinical and clinical studies published over the last ten years that examined the single effects of the most consumed NNS: aspartame, acesulfame-K, sucralose, and saccharin. Pre-clinical studies have given conflicting results for various reasons, including the administration method and the differences in metabolism of the same NNS among the different animal species. A dysbiotic effect of NNS was observed in some human trials, but many other randomized controlled trials reported a lack of significant impacts on gut microbiota composition. These studies differed in the number of subjects involved, their dietary habits, and their lifestyle; all factors related to the baseline composition of gut microbiota and their response to NNS. The scientific community still has no unanimous consensus on the appropriate outcomes and biomarkers that can accurately define the effects of NNS on the gut microbiota.

The potential role of molecular mimicry by the anaerobic microbiome in the aetiology of autoimmune disease

Autoimmune diseases are thought to develop as a consequence of various environmental and genetic factors, each of which contributes to dysfunctional immune responses and/or a breakdown in immunological tolerance towards native structures. Molecular mimicry by microbial components is among the environmental factors thought to promote a breakdown in immune tolerance, particularly through the presence of cross-reactive epitopes shared with the human host. While resident members of the microbiome are essential promoters of human health through immunomodulation, defence against pathogenic colonisation and conversion of dietary fibre into nutritional resources for host tissues, there may be an underappreciated role of these microbes in the aetiology and/or progression of autoimmune disease. An increasing number of molecular mimics are being identified amongst the anaerobic microbiota which structurally resemble endogenous components and, in some cases, for example the human ubiquitin mimic of Bacteroides fragilis and DNA methyltransferase of Roseburia intestinalis, have been associated with promoting antibody profiles characteristic of autoimmune diseases. The persistent exposure of molecular mimics from the microbiota to the human immune system is likely to be involved in autoantibody production that contributes to the pathologies associated with immune-mediated inflammatory disorders. Here-in, examples of molecular mimics that have been identified among resident members of the human microbiome and their ability to induce autoimmune disease through cross-reactive autoantibody production are discussed. Improved awareness of the molecular mimics that exist among human colonisers will help elucidate the mechanisms involved in the breakdown of immune tolerance that ultimately lead to chronic inflammation and downstream disease.

Integrating microbiome and metabolome revealed microbe-metabolism interactions in the stomach of patients with different severity of peptic ulcer disease

BACKGROUND

Peptic ulcer disease (PUD) is a multi-cause illness with an unknown role for gastric flora and metabolism in its pathogenesis. In order to further understand the pathogenesis of gastric flora and metabolism in PUD, this study used histological techniques to analyze the microbiome and metabolome of gastric biopsy tissue. In this paper, our work described the complex interactions of phenotype-microbial-metabolite-metabolic pathways in PUD patients at different pathological stages.

METHODS

Gastric biopsy tissue samples from 32 patients with chronic non-atrophic gastritis, 24 patients with mucosal erosions, and 8 patients with ulcers were collected for the microbiome. UPLC-MS metabolomics was also used to detect gastric tissue samples. These datasets were analyzed individually and integrated using various bioinformatics methods.

RESULTS

Our work found reduced diversity of gastric flora in patients with PUD. PUD patients at different pathological stages presented their own unique flora, and there were significant differences in flora phenotypes. Coprococcus_2, Phenylobacterium, Candidatus_Hepatoplasma, and other bacteria were found in the flora of people with chronic non-atrophic gastritis (HC). The representative flora of mucosal erosion (ME) had uncultured_bacterium_c_Subgroup_6, Sphingomonadaceae, Xanthobacteraceae, and uncultured_bacterium_f_Xanthobacteraceae. In comparison, the characteristic flora of the PUD group was the most numerous and complex, including Ruminococcus_2, Agathobacter, Alistipes, Helicobacter, Bacteroides and Faecalibacterium. Metabolomics identified and annotated 66 differential metabolites and 12 significantly different metabolic pathways. The comprehensive analysis correlated microorganisms with metabolites at different pathological stages and initially explored the complex interactions of phenotype-microbial-metabolite-metabolic pathways in PUD patients at different pathological stages.

CONCLUSION

Our research results provided substantial evidence to support some data on the analysis of the microbial community and its metabolism in the stomach, and they demonstrated many specific interactions between the gastric microbiome and the metabolome. Our study can help reveal the pathogenesis of PUD and indicate plausible disease-specific mechanisms for future studies from a new perspective.

Defining the Role of the Gut Microbiome in the Pathogenesis and Treatment of Lymphoid Malignancies

The gut microbiome is increasingly being recognized as an important immunologic environment, with direct links to the host immune system. The scale of the gut microbiome's genomic repertoire extends the capacity of its host's genome by providing additional metabolic output, and the close communication between gut microbiota and mucosal immune cells provides a continued opportunity for immune education. The relationship between the gut microbiome and the host immune system has important implications for oncologic disease, including lymphoma, a malignancy derived from within the immune system itself. In this review, we explore past and recent discoveries describing the role that bacterial populations play in lymphomagenesis, diagnosis, and therapy. We highlight key relationships within the gut microbiome-immune-oncology axis that present exciting opportunities for directed interventions intended to shape the microbiome for therapeutic effect. We conclude with a limited summary of active clinical trials targeting the microbiome in hematologic malignancies, along with future directions on gut microbiome investigations within lymphoid malignancies.

Application of emerging technologies for gut microbiome research

Microbiome is associated with a wide range of diseases. The gut microbiome is also a dynamic reflection of health status, which can be modified, thus representing great potential to exploit the mechanisms that influence human physiology. Recent years have seen a dramatic rise in gut microbiome studies, which has been enabled by the rapidly evolving high-throughput sequencing methods (i.e. 16S rRNA sequencing and shotgun sequencing). As the emerging technologies for microbiome research continue to evolve (i.e. metatranscriptomics, metabolomics, culturomics, synthetic biology), microbiome research has moved beyond phylogenetic descriptions and towards mechanistic analyses. In this review, we highlight different approaches to study the microbiome, in particular, the current limitations and future promise of these techniques. This review aims to provide clinicians with a framework for studying the microbiome, as well as to accelerate the adoption of these techniques in clinical practice.

An exploratory study of the gut microbiota in major depression with anxious distress

OBJECTIVES

To explore differences in the diversity and composition of the gut microbiome between major depressive disorder (MDD) with and without anxious distress.

METHODS

The study comprised 117 participants (79 female, 36 male, 2 other, mean age 38.2 ± 13.4 years) with a current major depressive episode (MDE) with (n = 63) and without (n = 54) the anxious distress specifier. A clinical psychologist administered the structured clinical interview for the DSM-5-RV to confirm a diagnosis of depression. Participants provided stool samples which were immediately frozen and stored at -80 °C. These samples were analysed using the Illumina 16S Metagenomics sequencing protocol in which the sequencing primers target the V3 and V4 regions of the 16S rRNA gene. Participants also completed mental health questionnaires to assess severity of depression (BDI-II), generalized anxiety (GAD-7), and stress (PSS).

RESULTS

There were no significant group differences in α-diversity (Shannon's diversity Index; Simpson Index), richness (ACE; Chao1), (Pielou's) evenness, or beta diversity (Bray-Curtis dissimilarity index and weighted UniFrac distance) of gut bacteria. Significant group differences in the relative abundance of gut microbiota however were observed at each taxonomical level, including across 15 genera and 18 species.

LIMITATIONS

This was an exploratory study that needs to be replicated across larger samples and compared with a healthy control group.

CONCLUSIONS

The research contributes to knowledge of the depressive gut microbial profile unique to the anxious distress subtype of MDD.

The bidirectional relationship between endometriosis and microbiome

Endometriosis has been described by many different theories of pathogenesis over the years. It is now also appreciated to be a state of chronic inflammation, and the role of immune dysfunction in its development has been proven. There is increasing evidence to support the role of the microbiome in the formation and progression of endometriosis via inflammatory pathways. The dysbiosis seen in endometriosis is thought to be both causative and a consequence of the pathogenesis. Gut, peritoneal fluid and female reproductive tract microbiota has been studied to understand if there are any microbiome signatures specific to endometriosis. New research on how to manipulate the microbiome for better detection and treatment of endometriosis is emerging.

Analysis of correlations between gut microbiota, stool short chain fatty acids, calprotectin and cardiometabolic risk factors in postmenopausal women with obesity: a cross-sectional study

BACKGROUND

Microbiota and its metabolites are known to regulate host metabolism. In cross-sectional study conducted in postmenopausal women we aimed to assess whether the microbiota, its metabolites and gut barrier integrity marker are correlated with cardiometabolic risk factors and if microbiota is different between obese and non-obese subjects.

METHODS

We analysed the faecal microbiota of 56 obese, postmenopausal women by means of 16S rRNA analysis. Stool short chain fatty acids, calprotectin and anthropometric, physiological and biochemical parameters were correlates to microbiome analyses.

RESULTS

Alpha-diversity was inversely correlated with lipopolysaccharide (Rho = - 0.43, FDR P (Q) = 0.004). Bray-Curtis distance based RDA revealed that visceral fat and waist circumference had a significant impact on metabolic potential (P = 0.003). Plasma glucose was positively correlated with the Coriobacteriaceae (Rho = 0.48, Q = 0.004) and its higher taxonomic ranks, up to phylum (Actinobacteria, Rho = 0.46, Q = 0.004). At the metabolic level, the strongest correlation was observed for the visceral fat (Q < 0.15), especially with the DENOVOPURINE2-PWY, PWY-841 and PWY0-162 pathways. Bacterial abundance was correlated with SCFAs, thus some microbiota-glucose relationships may be mediated by propionate, as indicated by the significant average causal mediation effect (ACME): Lachnospiraceae (ACME 1.25, 95%CI (0.10, 2.97), Firmicutes (ACME 1.28, 95%CI (0.23, 3.83)) and Tenericutes (ACME - 0.39, 95%CI (- 0.87, - 0.03)). There were significant differences in the distribution of phyla between this study and Qiita database (P < 0.0001).

CONCLUSIONS

Microbiota composition and metabolic potential are associated with some CMRF and fecal SCFAs concentration in obese postmenopausal women. There is no unequivocal relationship between fecal SCFAs and the marker of intestinal barrier integrity and CMRF. Further studies with appropriately matched control groups are warranted to look for causality between SCFAs and CMRF.

Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes

As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.

Microbe-based therapies for colorectal cancer: Advantages and limitations

Cancer is one of the leading global causes of death in both men and women. Colorectal cancer (CRC) alone accounts for ∼10 % of total new global cases and poses an over 4% lifetime risk of developing cancer. Recent advancements in the field of biotechnology and microbiology concocted novel microbe-based therapies to treat various cancers, including CRC. Microbes have been explored for human use since centuries, especially for the treatment of various ailments. The utility of microbes in cancer therapeutics is widely explored, and various bacteria, fungi, and viruses are currently in use for the development of cancer therapeutics. The human gut hosts about 100 trillion microbes that release their metabolites in active, inactive, or dead conditions. Microbial secondary metabolites, proteins, immunotoxins, and enzymes are used to target cancer cells to induce cell cycle arrest, apoptosis, and death. Various approaches, such as dietary interventions, the use of prebiotics and probiotics, and fecal microbiota transplantation have been used to modulate the gut microbiota in order to prevent or treat CRC pathogenesis. The present review highlights the role of the gut microbiota in CRC precipitation, the potential mechanisms and use of microorganisms as CRC biomarkers, and strategies to modulate microbiota for the prevention and treatment of CRC.

A Host-Directed Approach to the Detection of Infection in Hard-to-Heal Wounds

Wound infection is traditionally defined primarily by visual clinical signs, and secondarily by microbiological analysis of wound samples. However, these approaches have serious limitations in determining wound infection status, particularly in early phases or complex, chronic, hard-to-heal wounds. Early or predictive patient-derived biomarkers of wound infection would enable more timely and appropriate intervention. The observation that immune activation is one of the earliest responses to pathogen activity suggests that immune markers may indicate wound infection earlier and more reliably than by investigating potential pathogens themselves. One of the earliest immune responses is that of the innate immune cells (neutrophils) that are recruited to sites of infection by signals associated with cell damage. During acute infection, the neutrophils produce oxygen radicals and enzymes that either directly or indirectly destroy invading pathogens. These granular enzymes vary with cell type but include elastase, myeloperoxidase, lysozyme, and cathepsin G. Various clinical studies have demonstrated that collectively, these enzymes, are sensitive and reliable markers of both early-onset phases and established infections. The detection of innate immune cell enzymes in hard-to-heal wounds at point of care offers a new, simple, and effective approach to determining wound infection status and may offer significant advantages over uncertainties associated with clinical judgement, and the questionable value of wound microbiology. Additionally, by facilitating the detection of early wound infection, prompt, local wound hygiene interventions will likely enhance infection resolution and wound healing, reduce the requirement for systemic antibiotic therapy, and support antimicrobial stewardship initiatives in wound care.

Infections and Changes in Commensal Bacteria and the Pathogenesis of Parkinson’s Disease

The cause of Parkinson’s disease (PD) is unknown, but environmental factors are purported to influence risk. Interest in PD as a sequel of infection dates back to reports of parkinsonism arising from encephalitis lethargica. The objective of this paper is to review the literature as it relates to infections and changes in microbiome and the genesis of PD. There is evidence to support prior infection with Helicobacter pylori, hepatitis C virus, Malassezia, and Strep pneumonia in association with PD. A large number of studies support an association between changes in commensal bacteria, especially gut bacteria, and PD. Extant literature supports a role for some infections and changes in commensal bacteria in the genesis of PD. Studies support an inflammatory mechanism for this association, but additional research is required for translation of these findings to therapeutic options.

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.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

Sex-Dependent Effects of Inhaled Nicotine on the Gut Microbiome

INTRODUCTION

The impact of nicotine, the addictive component of both traditional cigarettes and e-cigarettes, on many physiological processes remains poorly understood. To date, there have been few investigations into the impact of nicotine on the gut microbiome, and these studies utilized oral administration rather than inhalation. This study aimed to establish if inhaled nicotine alters the gut microbiome and the effect of sex as a biological variable.

METHODS

Female (n = 8 air; n = 10 nicotine) and male (n = 10 air; n = 10 nicotine) C57BL6/J mice were exposed to air (control) or nicotine vapor (12 hour/day) for 13 weeks. A fecal sample was collected from each mouse at the time of sacrifice, and the gut microbiome was analyzed by 16S rRNA gene sequencing. QIIME2, PICRUSt, and STAMP were used to detect gut bacterial differences and functional metabolic pathways.

RESULTS

Sex-specific differences were observed in both alpha and beta diversities in the absence of nicotine. While nicotine alters microbial community structure in both male and female mice as revealed by the beta diversity metric, nicotine significantly reduced alpha diversity only in female mice. A total of 42 bacterial taxa from phylum to species were found to be significantly different among the treatment groups. Finally, analysis for functional genes revealed significant differences in twelve metabolic pathways in female mice and ten in male mice exposed to nicotine compared to air controls.

CONCLUSIONS

Nicotine inhalation alters the gut microbiome and reduces bacterial diversity in a sex-specific manner, which may contribute to the overall adverse health impact of nicotine.

IMPLICATIONS

The gut microbiota plays a fundamental role in the well-being of the host, and traditional cigarette smoking has been shown to affect the gut microbiome. The effects of nicotine alone, however, remain largely uncharacterized. Our study demonstrates that nicotine inhalation alters the gut microbiome in a sex-specific manner, which may contribute to the adverse health consequences of inhaled nicotine. This study points to the importance of more detailed investigations into the influence of inhaled nicotine on the gut microbiota.

A comparison of faecal glucocorticoid metabolite concentration and gut microbiota diversity in bonobos (Pan paniscus)

Sex, age, diet, stress and social environment have all been shown to influence the gut microbiota. In several mammals, including humans, increased stress is related to decreasing gut microbial diversity and may differentially impact specific taxa. Recent evidence from gorillas shows faecal glucocorticoid metabolite concentration (FGMC) did not significantly explain gut microbial diversity, but it was significantly associated with the abundance of the family Anaerolineaceae. These patterns have yet to be examined in other primates, like bonobos (Pan paniscus). We compared FGMC to 16S rRNA amplicons for 202 bonobo faecal samples collected across 5 months to evaluate the impact of stress, measured with FGMC, on the gut microbiota. Alpha diversity measures (Chao's and Shannon's indexes) were not significantly related to FGMC. FGMC explained 0.80 % of the variation in beta diversity for Jensen-Shannon and 1.2% for weighted UniFrac but was not significant for unweighted UniFrac. We found that genus SHD-231, a member of the family Anaerolinaceae had a significant positive relationship with FGMC. These results suggest that bonobos are relatively similar to gorillas in alpha diversity and family Anaerolinaceae responses to FGMC, but different from gorillas in beta diversity. Members of the family Anaerolinaceae may be differentially affected by FGMC across great apes. FGMC appears to be context dependent and may be species-specific for alpha and beta diversity but this study provides an example of consistent change in two African apes. Thus, the relationship between physiological stress and the gut microbiome may be difficult to predict, even among closely related species.

Statistical challenges in longitudinal microbiome data analysis

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

Level of injury is an independent determining factor of gut dysbiosis in people with chronic spinal cord injury: A cross-sectional study

STUDY DESIGN

A cross-sectional study.

OBJECTIVE

To investigate the correlations between gut microbiota and metabolic parameters in people with different levels of chronic spinal cord injury (SCI).

SETTING

An SCI-specialized rehabilitation facility in a university hospital.

METHODS

Forty-three participants with chronic SCI were recruited. Blood samples of each participant were collected for analysis of metabolic parameters. Feces were collected after the bowel opening method the patient routinely uses to evaluate fecal bacterial microbiota using quantitative RT-PCR. Body composition was examined using dual-energy x-ray absorptiometry (DEXA). Data were analyzed to evaluate the correlations between gut microbiota and other parameters.

RESULTS

Of the 43 participants, 31 people (72.1%) were paraplegic and 12 people (27.9%) tetraplegic. Thirty-two people (74.4%) were diagnosed with obesity using the percentage of body fat (% body fat) criteria. The mean (SD) ratio of Firmicutes:Bacteroides (F/B), which represents the degree of gut dysbiosis, was 18.3 (2.45). Using stepwise multivariable linear regression analysis, both having tetraplegia and being diagnosed with obesity from % body fat evaluated by DEXA were independent positively-correlating factors of F/B (p < 0.001 and p = 0.001, respectively), indicating more severe gut dysbiosis in people with tetraplegia than paraplegia.

CONCLUSION

In people with chronic SCI, having tetraplegia and being diagnosed with obesity from % body fat evaluated by DEXA are independent positive-correlating factors of gut dysbiosis. These results indicate a significant association between gut microbiota and the characteristics of SCI as well as metabolic parameters.

Distinctive Clinical Correlates of Small Intestinal Bacterial Overgrowth with Methanogens

BACKGROUND AND AIMS

Most patients with small intestinal bacterial overgrowth (SIBO) produce hydrogen by fermentation of dietary carbohydrates; however, ∼30% of patients with SIBO are colonized with Archaea, anaerobic organisms that produce methane. SIBO is associated with a plethora of symptoms and conditions, but their diagnostic significance is unclear. We aimed to determine if specific symptoms and conditions are associated with methanogenic SIBO.

METHODS

This study received institutional review board approval (IRB00059873). In this retrospective cross-sectional study, we queried a database of glucose breath tests conducted for suspected SIBO at our tertiary care medical center, which included data on the presence or absence of gastrointestinal symptoms and conditions often associated with SIBO. All patients had undergone a standardized breath testing protocol.

RESULTS

In a cohort of 1461 patients, 33.1% were SIBO positive; of these, 49.8% produced only hydrogen, 38.8% produced only methane, and 11.4% produced both gases. The following factors distinguished patients with hydrogen-producing SIBO, but not methanogenic SIBO, from SIBO-negative patients: vitamin B₁₂ deficiency (odds ratio, 1.44; confidence interval [CI], 1.01-2.06; P = .046), Roux-en-Y gastric bypass (odds ratio, 2.14; CI, 1.09-4.18; P = .027), cholecystectomy (odds ratio, 1.42; CI, 1.06-1.91; P = .020), and diabetes (odds ratio, 1.59; CI, 1.13-2.24; P = .008). The absence of vitamin B₁₂ deficiency was the sole discriminating factor between methanogenic and hydrogenic SIBO (odds ratio, 0.57; CI, 0.34-0.97; P = .038).

CONCLUSIONS

Patients with SIBO caused by methane-producing Archaea display a different spectrum of associated symptoms and clinical conditions compared with patients with SIBO caused by hydrogen-producing bacteria, particularly a lower incidence of vitamin B₁₂ deficiency.

Optimizing UniFrac with OpenACC Yields Greater Than One Thousand Times Speed Increase

UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another (beta diversity). Striped UniFrac recently added the ability to split the problem into many independent subproblems, exhibiting nearly linear scaling but suffering from memory contention. Here, we adapt UniFrac to graphics processing units using OpenACC, enabling greater than 1,000× computational improvement, and apply it to 307,237 samples, the largest 16S rRNA V4 uniformly preprocessed microbiome data set analyzed to date. IMPORTANCE UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. To highlight this advance, we perform what may be the largest microbiome analysis to date, applying UniFrac to 307,237 16S rRNA V4 microbiome samples preprocessed with Deblur. These scaling improvements turn UniFrac into a real-time tool for common data sets and unlock new research questions as more microbiome data are collected.

Deep Learning Encoding for Rapid Sequence Identification on Microbiome Data

We present a novel approach for rapidly identifying sequences that leverages the representational power of Deep Learning techniques and is applied to the analysis of microbiome data. The method involves the creation of a latent sequence space, training a convolutional neural network to rapidly identify sequences by mapping them into that space, and we leverage the novel encoded latent space for denoising to correct sequencing errors. Using mock bacterial communities of known composition, we show that this approach achieves single nucleotide resolution, generating results for sequence identification and abundance estimation that match the best available microbiome algorithms in terms of accuracy while vastly increasing the speed of accurate processing. We further show the ability of this approach to support phenotypic prediction at the sample level on an experimental data set for which the ground truth for sequence identities and abundances is unknown, but the expected phenotypes of the samples are definitive. Moreover, this approach offers a potential solution for the analysis of data from other types of experiments that currently rely on computationally intensive sequence identification.

Variability in the Pre-Analytical Stages Influences Microbiome Laboratory Analyses

Introduction: There are numerous confounding variables in the pre-analytical steps in the analysis of gut microbial composition that affect data consistency and reproducibility. This study compared two DNA extraction methods from the same faecal samples to analyse differences in microbial composition. Methods: DNA was extracted from 20 faecal samples using either (A) chemical/enzymatic heat lysis (lysis buffer, proteinase K, 95 °C + 70 °C) or (B) mechanical and chemical/enzymatic heat lysis (bead-beating, lysis buffer, proteinase K, 65 °C). Gut microbiota was mapped through the 16S rRNA gene (V3−V9) using a set of pre-selected DNA probes targeting >300 bacteria on different taxonomic levels. Apart from the pre-analytical DNA extraction technique, all other parameters including microbial analysis remained the same. Bacterial abundance and deviations in the microbiome were compared between the two methods. Results: Significant variation in bacterial abundance was seen between the different DNA extraction techniques, with a higher yield of species noted in the combined mechanical and heat lysis technique (B). The five predominant bacteria seen in both (A) and (B) were Bacteroidota spp. and Prevotella spp. (p = NS), followed by Bacillota (p = 0.005), Lachhnospiraceae (p = 0.0001), Veillonella spp. (p < 0.0001) and Clostridioides (p < 0.0001). Conclusion: As microbial testing becomes more easily and commercially accessible, a unified international consensus for optimal sampling and DNA isolation procedures must be implemented for robustness and reproducibility of the results.

The rise to power of the microbiome: power and sample size calculation for microbiome studies

A priori power and sample size calculations are crucial to appropriately test null hypotheses and obtain valid conclusions from all clinical studies. Statistical tests to evaluate hypotheses in microbiome studies need to consider intrinsic features of microbiome datasets that do not apply to classic sample size calculation. In this review, we summarize statistical approaches to calculate sample sizes for typical microbiome study scenarios, including those that hypothesize microbiome features to be the outcome, the exposure or the mediator, and provide relevant R scripts to conduct some of these calculations. This review is intended to be a resource to facilitate the conduct of sample size calculations that are based on testable hypotheses across several dimensions of the microbiome. Implementation of these methods will improve the quality of human or animal microbiome studies, enabling reliable conclusions that will generalize beyond the study sample.

Promises of microbiome-based therapies

Humans harbour large quantities of microbes, including bacteria, fungi, viruses and archaea, in the gut. Patients with liver disease exhibit changes in the intestinal microbiota and gut barrier dysfunction. Preclinical models demonstrate the importance of the gut microbiota in the pathogenesis of various liver diseases. In this review, we discuss how manipulation of the gut microbiota can be used as a novel treatment approach for liver disease. We summarise current data on untargeted approaches, including probiotics and faecal microbiota transplantation, and precision microbiome-centered therapies, including engineered bacteria, postbiotics and phages, for the treatment of liver diseases.

Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets

The disruption of the human gut microbiota has been linked to host health conditions, including various diseases. However, no reliable index for measuring and predicting a healthy microbiome is currently available. Here, the sequencing data of 1,663 Koreans were obtained from three independent studies. Furthermore, we pooled 3,490 samples from public databases and analyzed a total of 5,153 fecal samples. First, we analyzed Korean gut microbiome covariates to determine the influence of lifestyle on variation in the gut microbiota. Next, patterns of microbiota variations across geographical locations and disease statuses were confirmed using a global cohort and di-sease data. Based on comprehensive comparative analysis, we were able to define three enterotypes among Korean cohorts, namely, Prevotella type, Bacteroides type, and outlier type. By a thorough categorization of dysbiosis and the evaluation of microbial characteristics using multiple datasets, we identified a wide spectrum of accuracy levels in classifying health and disease states. Using the observed microbiome patterns, we devised an index named the gut microbiome index (GMI) that could consistently predict health conditions from human gut microbiome data. Compared to ecological metrics, the microbial marker index, and machine learning approaches, GMI distinguished between healthy and non-healthy individuals with a higher accuracy across various datasets. Thus, this study proposes a potential index to measure health status of gut microbiome that is verified from multiethnic data of various diseases, and we expect this model to facilitate further clinical application of gut microbiota data in future.

Reduced Enterohepatic Recirculation of Mycophenolate and Lower Blood Concentrations are Associated with the Stool Bacterial Microbiome After Hematopoietic Cell Transplantation

BACKGROUND

Mycophenolate mofetil (MMF) is an important immunosuppressant used after allogeneic hematopoietic cell transplant (HCT). MMF has a narrow therapeutic index and blood concentrations of mycophenolic acid (MPA), the active component of MMF, are highly variable. Low MPA concentrations are associated with risk of graft vs host disease (GvHD) while high concentrations are associated with toxicity. Reasons for variability are not well known and may be due, at least in part, to the presence of β-glucuronidase producing bacteria in the gastrointestinal tract which enhance MPA enterohepatic recirculation (EHR) by transforming MPA metabolites formed in the liver back to MPA.

OBJECTIVE

To determine if individuals with high MPA EHR have a greater abundance of β-glucuronidase producing bacteria in their stool and higher MPA concentrations relative to those with low EHR.

STUDY DESIGN

We conducted a pharmacomicrobiomics study in 20 adult HCT recipients receiving a myeloablative or reduced intensity preparative regimen. Participants received MMF 1g IV every 8 hours with tacrolimus. Intensive pharmacokinetic sampling of mycophenolate was conducted before hospital discharge. Total MPA, MPA glucuronide (MPAG) and acylMPAG were measured. EHR was defined as a ratio of MPA area under the concentration-versus-time curve (AUC)_4-8 to MPA AUC_0-8. Differences in stool microbiome diversity and composition, determined by shotgun metagenomic sequencing, were compared above and below the median EHR (22%, range 5-44%).

RESULTS

Median EHR was 12% and 29% in the low and high EHR groups, respectively. MPA troughs, MPA AUC_4-8 and acylMPAG AUC_4-8/AUC_0-8, were greater in the high EHR group vs low EHR group [1.53 vs 0.28 mcg/mL, p = 0.0001], [7.33 vs 1.79 hr*mcg/mL, p = 0.0003] and [0.33 vs 0.24 hr*mcg/mL, p = 0.0007], respectively. MPA AUC_0-8 was greater in the high EHR than the low EHR group and trended towards significance [22.8 vs. 15.3 hr*mcg/mL, p=0.06]. Bacteroides vulgatus, stercoris and thetaiotaomicron were 1.2-2.4 times more abundant (p=0.039, 0.024, 0.046, respectively) in the high EHR group. MPA EHR was positively correlated with B. vulgatus (⍴=0.58, p≤0.01) and B. thetaiotaomicron (⍴=0.46, p<0.05) and negatively correlated with Blautia hydrogenotrophica (⍴=-0.53, p<0.05). Therapeutic MPA troughs were achieved in 80% of patients in the high EHR group and 0% in the low EHR. There was a trend towards differences in MPA AUC_0-8 and MPA C_ss mcg/mL in high vs. low EHR groups (p=0.06).

CONCLUSION

MPA EHR was variable. Patients with high MPA EHR had greater abundance of Bacteroides species in stool and higher MPA exposure than patients with low MPA EHR. Bacteroides may therefore be protective from poor outcomes such as graft vs host disease but in others it may increase the risk of MPA adverse effects. These data need to be confirmed and studied after oral MMF.

An optimized approach for processing of frozen lung and lavage samples for microbiome studies

The respiratory tract has a resident microbiome with low biomass and limited diversity. This results in difficulties with sample preparation for sequencing due to uneven bacteria-to-host DNA ratio, especially for small tissue samples such as mouse lungs. We compared effectiveness of current procedures used for DNA extraction in microbiome studies. Bronchoalveolar lavage fluid (BALF) and lung tissue samples were collected to test different forms of sample pre-treatment and extraction methods to increase bacterial DNA yield and optimize library preparation. DNA extraction using a pre-treatment method of mechanical lysis (lung tissue) and one-step centrifugation (BALF) increased DNA yield and bacterial content of samples. In contrast, a significant increase of environmental contamination was detected after phenol chloroform isoamyl alcohol (PCI) extraction and nested PCR. While PCI has been a standard procedure used in microbiome studies, our data suggests that it is not efficient for DNA extraction of frozen low biomass samples. Finally, a DNA Enrichment kit was tested and found to improve the 16S copy number of lung tissue with a minor shift in microbial composition. Overall, we present a standardized method to provide high yielding DNA and improve sequencing coverage of low microbial biomass frozen samples with minimal contamination.

Gut microbiota of elite female football players is not altered during an official international tournament

The current study aimed to investigate if the gut microbiota composition of elite female football players changes during an official international tournament. The study was conducted throughout ten consecutive days, encompassing seven training sessions, and three official matches. The matches were separated by 48-72 h. Seventeen elite female football players from the Portuguese women's national football team participated in the study. Fecal samples were collected at two time points: at the beginning and end of the tournament. Fecal microbiota was analyzed by sequencing the 16S rRNA gene. Throughout the study, the duration and rating of perceived exertion (RPE) were recorded after training sessions and matches. The internal load was determined by the session RPE. The gut microbiota of players was predominantly composed of bacteria from the phyla Firmicutes (50% of relative abundance) and Bacteroidetes (20%); the genera Faecalibacterium (29%) and Collinsella (16%); the species Faecalibacterium prausnitzii (30%) and Collinsella aerofaciens (17%). Overall, no significant changes were observed between time points (p ≥ 0.05). Also, no relationship was found between any exercise parameter and the gut microbiota composition (p ≥ 0.05). These findings demonstrate that the physical and physiological demands of training and matches of an official international tournament did not change the gut microbiota composition of elite female football players. Furthermore, it supports that the gut microbiota of athletes appears resilient to the physical and physiological demands of training and match play.

Overview of paratransgenesis as a strategy to control pathogen transmission by insect vectors

This article presents an overview of paratransgenesis as a strategy to control pathogen transmission by insect vectors. It first briefly summarises some of the disease-causing pathogens vectored by insects and emphasises the need for innovative control methods to counter the threat of resistance by both the vector insect to pesticides and the pathogens to therapeutic drugs. Subsequently, the state of art of paratransgenesis is described, which is a particularly ingenious method currently under development in many important vector insects that could provide an additional powerful tool for use in integrated pest control programmes. The requirements and recent advances of the paratransgenesis technique are detailed and an overview is given of the microorganisms selected for genetic modification, the effector molecules to be expressed and the environmental spread of the transgenic bacteria into wild insect populations. The results of experimental models of paratransgenesis developed with triatomines, mosquitoes, sandflies and tsetse flies are analysed. Finally, the regulatory and safety rules to be satisfied for the successful environmental release of the genetically engineered organisms produced in paratransgenesis are considered.

Jejunoileal mucosal growth in mice with a limited microbiome

Previous work demonstrated enhanced enterocyte proliferation and mucosal growth in gnotobiotic mice, suggesting that intestinal flora participate in mucosal homeostasis. Furthermore, broad-spectrum enteral antibiotics are known to induce near germ-free (GF) conditions in mice with conventional flora (CONV). We hypothesized that inducing near GF conditions with broad-spectrum enteral antibiotics would cause ordered small intestinal mucosal growth in CONV mice but would have no effect in GF mice with no inherent microbiome. C57BL/6J CONV and GF mice received either an antibiotic solution (Ampicillin, Ciprofloxacin, Metronidazole, Vancomycin, Meropenem) or a vehicle alone. After treatment, small intestinal villus height (VH), crypt depth (CD), mucosal surface area (MSA), crypt proliferation index (CPI), apoptosis, and villus and crypt cell types were assessed. Antibiotic-treated CONV (Abx-CONV) mice had taller villi, deeper crypts, increased CPI, increased apoptosis, and greater MSA compared to vehicle-treated CONV mice. Minor differences were noted in enterocyte and enterochromaffin cell proportions between groups, but goblet and Paneth cell proportions were unchanged in Abx-CONV mice compared to vehicle-treated CONV mice (p>0.05). Antibiotics caused no significant changes in VH or MSA in GF mice when compared to vehicle-treated GF mice (p>0.05). Enteral administration of broad-spectrum antibiotics to mice with a conventional microbiome stimulates ordered small intestinal mucosal growth. Mucosal growth was not seen in germ-free mice treated with antibiotics, implying that intestinal mucosal growth is associated with change in the microbiome in this model.

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.

Treatment With Multi-Species Probiotics Changes the Functions, Not the Composition of Gut Microbiota in Postmenopausal Women With Obesity: A Randomized, Double-Blind, Placebo-Controlled Study

Probiotics are known to regulate host metabolism. In randomized controlled trial we aimed to assess whether interventions with probiotic containing following strains: Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Levilactobacillus brevis W63, Lacticaseibacillus casei W56, Ligilactobacillus salivarius W24, Lactococcus lactis W19, and Lactococcus lactis W58 affect gut microbiota to promote metabolic effects. By 16S rRNA sequencing we analyzed the fecal microbiota of 56 obese, postmenopausal women randomized into three groups: (1) probiotic dose 2.5 × 10⁹ CFU/day (n = 18), (2) 1 × 10¹⁰ CFU/day (n = 18), or (3) placebo (n = 20). In the set of linear mixed-effects models, the interaction between pre- or post-treatment bacterial abundance and time on cardiometabolic parameters was significantly (FDR-adjusted) modified by type of intervention (26 and 19 three-way interactions for the pre-treatment and post-treatment abundance, respectively), indicating the modification of the bio-physiological role of microbiota by probiotics. For example, the unfavorable effects of Erysipelotrichi, Erysipelotrichales, and Erysipelotrichaceae on BMI might be reversed, but the beneficial effect of Betaproteobacteria on BMI was diminished by probiotic treatment. Proinflammatory effect of Bacteroidaceae was alleviated by probiotic administration. However, probiotics did not affect the microbiota composition, and none of the baseline microbiota-related features could predict therapeutic response as defined by cluster analysis. Conclusions: Probiotic intervention alters the influence of microbiota on biochemical, physiological and immunological parameters, but it does not affect diversity and taxonomic composition. Baseline microbiota is not a predictor of therapeutic response to a multispecies probiotic. Further multi-omic and mechanistic studies performed on the bigger cohort of patients are needed to elucidate the cardiometabolic effect of investigated probiotics in postmenopausal obesity.

Role of the Microbiome in Regulating Bone Metabolism and Susceptibility to Osteoporosis

The human microbiota functions at the interface between diet, medication-use, lifestyle, host immune development and health. It is therefore closely aligned with many of the recognised modifiable factors that influence bone mass accrual in the young, and bone maintenance and skeletal decline in older populations. While understanding of the relationship between micro-organisms and bone health is still in its infancy, two decades of broader microbiome research and discovery supports a role of the human gut microbiome in the regulation of bone metabolism and pathogenesis of osteoporosis as well as its prevention and treatment. Pre-clinical research has demonstrated biological interactions between the microbiome and bone metabolism. Furthermore, observational studies and randomized clinical trials have indicated that therapeutic manipulation of the microbiota by oral administration of probiotics may influence bone turnover and prevent bone loss in humans. In this paper, we summarize the content, discussion and conclusions of a workshop held by the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society in October, 2020. We provide a detailed review of the literature examining the relationship between the microbiota and bone health in animal models and in humans, as well as formulating the agenda for key research priorities required to advance this field. We also underscore the potential pitfalls in this research field that should be avoided and provide methodological recommendations to facilitate bridging the gap from promising concept to a potential cause and intervention target for osteoporosis.

Stool microbiota are superior to saliva in distinguishing cirrhosis and hepatic encephalopathy using machine learning

BACKGROUND & AIMS

Saliva and stool microbiota are altered in cirrhosis. Since stool is logistically difficult to collect compared to saliva, it is important to determine their relative diagnostic and prognostic capabilities. We aimed to determine the ability of stool vs. saliva microbiota to differentiate between groups based on disease severity using machine learning (ML).

METHODS

Controls and outpatients with cirrhosis underwent saliva and stool microbiome analysis. Controls vs. cirrhosis and within cirrhosis (based on hepatic encephalopathy [HE], proton pump inhibitor [PPI] and rifaximin use) were classified using 4 ML techniques (random forest [RF], support vector machine, logistic regression, and gradient boosting) with AUC comparisons for stool, saliva or both sample types. Individual microbial contributions were computed using feature importance of RF and Shapley additive explanations. Finally, thresholds for including microbiota were varied between 2.5% and 10%, and core microbiome (DESeq2) analysis was performed.

RESULTS

Two hundred and sixty-nine participants, including 87 controls and 182 patients with cirrhosis, of whom 57 had HE, 78 were on PPIs and 29 on rifaximin were included. Regardless of the ML model, stool microbiota had a significantly higher AUC in differentiating groups vs. saliva. Regarding individual microbiota: autochthonous taxa drove the difference between controls vs. patients with cirrhosis, oral-origin microbiota the difference between PPI users/non-users, and pathobionts and autochthonous taxa the difference between rifaximin users/non-users and patients with/without HE. These were consistent with the core microbiome analysis results.

CONCLUSIONS

On ML analysis, stool microbiota composition is significantly more informative in differentiating between controls and patients with cirrhosis, and those with varying cirrhosis severity, compared to saliva. Despite logistic challenges, stool should be preferred over saliva for microbiome analysis.

LAY SUMMARY

Since it is harder to collect stool than saliva, we wanted to test whether microbes from saliva were better than stool in differentiating between healthy people and those with cirrhosis and, among those with cirrhosis, those with more severe disease. Using machine learning, we found that microbes in stool were more accurate than saliva alone or in combination, therefore, stool should be preferred for analysis and collection wherever possible.

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data

Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses.

Vaginal microbiome of women with adenomyosis: A case-control study

Immune dysregulation can involve invasion and survival of endometrial glands inside the myometrium of the adenomyosis. There is limited available data concerning alterations of the bacterial microbiome in the reproductive tract of adenomyosis women. The present cross-sectional age-matched study aims to compare vaginal microbiota between women with and without adenomyosis. We recruited women with adenomyosis (N = 40) and age-matched women without adenomyosis (N = 40) from the Departments of Obstetrics and Gynaecology, Ramathibodi Hospital Mahidol University, from August 2020 to January 2021. Vaginal swab samples were collected from the participants. DNA isolation and bacterial 16s rDNA gene sequencing and data analyses were then performed. Comparison of the diversity of vaginal microbiota, microbiota composition, and the operational taxonomic unit (OTU) between adenomyosis and non-adenomyosis (control) groups were undertaken. Data from 40 and 38 women with and without adenomyosis, respectively, were analyzed. Alpha-diversity analysis (Chao1 index) at the species level showed higher vaginal microbial richness in the adenomyosis group when compared with the control group (p = 0.006). The linear discriminant analysis effect size technique (LeFSe) indicated an elevated abundance of several vaginal microbial taxa in the adenomyosis group, including Alloscardovia, Oscillospirales, Ruminoccoccaceae, UCG_002, Oscillospiraceae, Enhydrobacter, Megamonas, Moraxellaceae, Subdoligranulum, Selenomonadaceae, and Faecalibacterium. On the other hand, an increase in the abundance of Megaspehera, Fastidiosipila, Hungateiclostridiaceae, and Clostridia was identified in the control group. Vaginal community state type (CST)-III and -IV were dominated in adenomyosis, while only CST-IV was dominated in the non-adenomyosis group. Lactobacillus was the most abundant vaginal microbial in both groups. In this study, the differences in vaginal microbiome profile were noted between adenomyosis and non-adenomyosis group. The increasing of microbial richness was associated with adenomyosis. Nevertheless, further investigations were required to elucidate the mechanisms and apply them for clinical implications.

Exercise for the Diabetic Gut-Potential Health Effects and Underlying Mechanisms

It can be assumed that changes in the gut microbiota play a crucial role in the development of type 2 diabetes mellitus (T2DM). It is generally accepted that regular physical activity is beneficial for the prevention and therapy of T2DM. Therefore, this review analyzes the effects of exercise training on the gut microbiota composition and the intestinal barrier function in T2DM. The current literature shows that regular exercise can influence the gut microbiota composition and the intestinal barrier function with ameliorative effects on T2DM. In particular, increases in the number of short-chain fatty acid (SCFA)-producing bacteria and improvements in the gut barrier integrity with reduced endotoxemia seem to be key points for positive interactions between gut health and T2DM, resulting in improvements in low-grade systemic inflammation status and glycemic control. However, not all aspects are known in detail and further studies are needed to further examine the efficacy of different training programs, the role of myokines, SCFA-producing bacteria, and SCFAs in the relevant metabolic pathways. As microbial signatures differ in individuals who respond differently to exercise training programs, one scientific focus could be the development of computer-based methods for the personalized analysis of the gut microbiota in the context of a microbiota/microbiome-based training program.

Gut microbiome in neuropsychiatric disorders

ABSTRACT Background: Neuropsychiatric disorders are a significant cause of death and disability worldwide. The mechanisms underlying these disorders include a constellation of structural, infectious, immunological, metabolic, and genetic etiologies. Advances in next-generation sequencing techniques have demonstrated that the composition of the enteric microbiome is dynamic and plays a pivotal role in host homeostasis and several diseases. The enteric microbiome acts as a key mediator in neuronal signaling via metabolic, neuroimmune, and neuroendocrine pathways. Objective: In this review, we aim to present and discuss the most current knowledge regarding the putative influence of the gut microbiome in neuropsychiatric disorders. Methods: We examined some of the preclinical and clinical evidence and therapeutic strategies associated with the manipulation of the gut microbiome. Results: targeted taxa were described and grouped from major studies to each disease. Conclusions: Understanding the complexity of these ecological interactions and their association with susceptibility and progression of acute and chronic disorders could lead to novel diagnostic biomarkers based on molecular targets. Moreover, research on the microbiome can also improve some emerging treatment choices, such as fecal transplantation, personalized probiotics, and dietary interventions, which could be used to reduce the impact of specific neuropsychiatric disorders. We expect that this knowledge will help physicians caring for patients with neuropsychiatric disorders.

The Serum and Fecal Metabolomic Profiles of Growing Kittens Treated with Amoxicillin/Clavulanic Acid or Doxycycline

Simple Summary

This study investigated the impact of antibiotic treatment οn the serum and fecal metabolome (the collection of all small molecules produced by the gut bacteria and the host) of young cats. Thirty 2-month-old cats with an upper respiratory tract infection were treated with either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days. In addition, another 15 control cats that did not receive antibiotics were included. Blood was collected on days 0 (before treatment), 20/28 (last day of treatment), and 300 (10 months after the end of treatment), while feces were collected on days 0, 20/28, 60, 120, and 300. Seven serum and fecal metabolites differed between cats treated with antibiotics and control cats at the end of treatment period. Ten months after treatment, no metabolites differed from healthy cats, suggesting that amoxicillin/clavulanic acid or doxycycline treatment only temporarily affects the abundance of the serum and fecal metabolome.

Abstract

The long-term impact of antibiotics on the serum and fecal metabolome of kittens has not yet been investigated. Therefore, the objective of this study was to evaluate the serum and fecal metabolome of kittens with an upper respiratory tract infection (URTI) before, during, and after antibiotic treatment and compare it with that of healthy control cats. Thirty 2-month-old cats with a URTI were randomly assigned to receive either amoxicillin/clavulanic acid for 20 days or doxycycline for 28 days, and 15 cats of similar age were enrolled as controls. Fecal samples were collected on days 0, 20/28, 60, 120, and 300, while serum was collected on days 0, 20/28, and 300. Untargeted and targeted metabolomic analyses were performed on both serum and fecal samples. Seven metabolites differed significantly in antibiotic-treated cats compared to controls on day 20/28, with two differing on day 60, and two on day 120. Alterations in the pattern of serum amino acids, antioxidants, purines, and pyrimidines, as well as fecal bile acids, sterols, and fatty acids, were observed in antibiotic-treated groups that were not observed in control cats. However, the alterations caused by either amoxicillin/clavulanic acid or doxycycline of the fecal and serum metabolome were only temporary and were resolved by 10 months after their withdrawal.

The Neglected Gut Microbiome: Fungi, Protozoa, and Bacteriophages in Inflammatory Bowel Disease

The gut microbiome has been implicated in the pathogenesis of inflammatory bowel disease (IBD). Studies suggest that the IBD gut microbiome is less diverse than that of the unaffected population, a phenomenon often referred to as dysbiosis. However, these studies have heavily focused on bacteria, while other intestinal microorganisms-fungi, protozoa, and bacteriophages-have been neglected. Of the nonbacterial microbes that have been studied in relation to IBD, most are thought to be pathogens, although there is evidence that some of these species may instead be harmless commensals. In this review, we discuss the nonbacterial gut microbiome of IBD, highlighting the current biases, limitations, and outstanding questions that can be addressed with high-throughput DNA sequencing methods. Further, we highlight the importance of studying nonbacterial microorganisms alongside bacteria for a comprehensive view of the whole IBD biome and to provide a more precise definition of dysbiosis in patients. With the rise in popularity of microbiome-altering therapies for the treatment of IBD, such as fecal microbiota transplantation, it is important that we address these knowledge gaps to ensure safe and effective treatment of patients.