Microbiome Studies Research Articles

Duodenal microbiome in chronic kidney disease.

The intestinal microbiome is involved in the pathogenesis of chronic kidney disease (CKD). Despite its importance, the microbiome of the small intestinal mucosa has been little studied due to sampling difficulties, and previous studies have mainly focused on fecal sources for microbiome studies. We aimed to characterize the small intestinal microbiome of CKD patients by studying the microbiome collected from duodenal and fecal samples of CKD patients and healthy controls. Overall, 28 stage 5 CKD patients and 21 healthy participants were enrolled. Mucosal samples were collected from the deep duodenum during esophagogastroduodenoscopy and fecal samples were also collected. The 16S ribosomal RNA gene sequencing using Qiime2 was used to investigate and compare the microbial structure and metagenomic function of the duodenal and fecal microbiomes. The duodenal flora of CKD patients had decreased alpha diversity compared with the control group. On the basis of taxonomic composition, Veillonella and Prevotella were significantly reduced in the duodenal flora of CKD patients. The tyrosine and tryptophan metabolic pathways were enhanced in the urea toxin-related metabolic pathways based on the Kyoto Encyclopedia of Genes and Genomes database. The small intestinal microbiome in CKD patients is significantly altered, indicating that increased intestinal permeability and production of uremic toxin may occur in the upper small intestine of CKD patients.

Oncoprotective effects of chondroprotectors: glucosamine, chondroitin sulfate and undenatured type II collagen

Objective: to systematize fundamental, clinical, and epidemiological data on the oncoprotective effects of chondroprotectors: chondroitin sulfate (CS), glucosamine (including glucosamine sulfate, GS), and undenatured type II collagen (UC-II).Material and methods. A systematic computer analysis of 6176 publications on the relationship between CS/GS/UC-II and tumor diseases found by the query “(glucosamine OR chondroitin OR ((“Collagen Type II” OR “type II collagen”) AND pharmacology)) AND (Cancer OR cancers OR tumor OR tumors OR tumors OR tumour*) NOT tumor necrosis)” in PubMed and Embase databases was performed. All articles of any format from 1900 to the present day with full available abstracts were taken. A topological approach to data analysis was used.Results. Large-scale clinical and epidemiological studies and meta-analyses showed that regular consumption of CS/GS reduced the risk of colorectal cancer and lung cancer, as well as mortality from tumor diseases. The mechanisms of oncoprotective action of CS/GS are through inhibition of the pro-inflammatory cascade of tumor necrosis factor alpha, CD44 receptor and nuclear factor kappa B, and initiation of tumor cell apoptosis. By modulating the CD44 receptor and specific O-glycosylation of intracellular proteins, GS inhibits the pro-inflammatory effects of arachidonic acid cascade, interleukins IL-6, IL-8, the PI3K/Akt proliferative pathway, and cyclin-dependent kinases. The first postgenomic studies of CS/GS oncoprotective effects, including microbiome studies, was performed. Additionally, CS contributes to the inhibition of the effects of vascular endothelial growth factor and matrix metalloproteinases involved in tumor metastasis and invasion. Potentially, CS/GS oncoprotective effects may be enhanced by the anti-inflammatory effect of UC-II: the addition of NC-II substance to CS/GS complex makes it possible to reduce the autoimmune branch of pathogenesis not only in primary, but also in secondary OA and rheumatoid arthritis.Conclusion. The CS and glucosamine (including GS) chondroprotectors exhibit oncoprotective effects. The use of CS and GS together with UС-II standardized pharmaceutical forms can enhance their anti-inflammatory and immunomodulatory effects.

Multiomics Analyses With Stool-Type Stratification in Patient Cohorts and Blautia Identification as a Potential Bacterial Modulator in Type 2 Diabetes Mellitus.

Heterogeneity in host and gut microbiota hampers microbial precision intervention of type 2 diabetes mellitus (T2DM). Here, we investigated novel features for patient stratification and bacterial modulators for intervention, using cross-sectional patient cohorts and animal experiments. We collected stool, blood, and urine samples from 103 patients with recent-onset T2DM and 25 healthy control subjects (HCs), performed gut microbial composition and metabolite profiling, and combined it with host transcriptome, metabolome, cytokine, and clinical data. Stool type (dry or loose stool), a feature of the stool microenvironment recently explored in microbiome studies, was used for stratification of patients with T2DM as it explained most of the variation in the multiomics data set among all clinical parameters in our covariate analysis. T2DM with dry stool (DM-DS) and loose stool (DM-LS) were clearly differentiated from HC and each other by LightGBM models, optimal among multiple machine learning models. Compared with DM-DS, DM-LS exhibited discordant gut microbial taxonomic and functional profiles, severe host metabolic disorder, and excessive insulin secretion. Further cross-measurement association analysis linked the differential microbial profiles, in particular Blautia abundances, to T2DM phenotypes in our stratified multiomics data set. Notably, oral supplementation of Blautia to T2DM mice induced inhibitory effects on lipid accumulation, weight gain, and blood glucose elevation with simultaneous modulation of gut bacterial composition, revealing the therapeutic potential of Blautia. Our study highlights the clinical implications of stool microenvironment stratification and Blautia supplementation in T2DM, offering promising prospects for microbial precision treatment of metabolic diseases. Article Highlights

Unveiling microbial biomarkers of ruminant methane emission through machine learning.

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants. In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features. Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions. We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.

DNA from non-viable bacteria biases diversity estimates in the corals Acropora loripes and Pocillopora acuta

BackgroundNucleic acid-based analytical methods have greatly expanded our understanding of global prokaryotic diversity, yet standard metabarcoding methods provide no information on the most fundamental physiological state of bacteria, viability. Scleractinian corals harbour a complex microbiome in which bacterial symbionts play critical roles in maintaining health and functioning of the holobiont. However, the coral holobiont contains both dead and living bacteria. The former can be the result of corals feeding on bacteria, rapid swings from hyper- to hypoxic conditions in the coral tissue, the presence of antimicrobial compounds in coral mucus, and an abundance of lytic bacteriophages.ResultsBy combining propidium monoazide (PMA) treatment with high-throughput sequencing on six coral species (Acropora loripes, A. millepora, A. kenti, Platygyra daedalea, Pocillopora acuta, and Porites lutea) we were able to obtain information on bacterial communities with little noise from non-viable microbial DNA. Metabarcoding of the 16S rRNA gene showed significantly higher community evenness (85%) and species diversity (31%) in untreated compared with PMA-treated tissue for A. loripes only. While PMA-treated coral did not differ significantly from untreated samples in terms of observed number of ASVs, > 30% of ASVs were identified in untreated samples only, suggesting that they originated from cell-free/non-viable DNA. Further, the bacterial community structure was significantly different between PMA-treated and untreated samples for A. loripes and P. acuta indicating that DNA from non-viable microbes can bias community composition data in coral species with low bacterial diversity.ConclusionsOur study is highly relevant to microbiome studies on coral and other host organisms as it delivers a solution to excluding non-viable DNA in a complex community. These results provide novel insights into the dynamic nature of host-associated microbiomes and underline the importance of applying versatile tools in the analysis of metabarcoding or next-generation sequencing data sets.

Disentangling the age-related manner in the associations between gut microbiome and women’s health: a multi-cohort microbiome study

ABSTRACT Women’s health encompasses life-course healthcare, and mounting evidence emphasizes the pivotal contribution of gut microbiota. Therefore, understanding the temporal dynamics of gut microbiota and how age influences disease-gut microbiota associations is essential for improving women’s health. By analyzing metagenomic data from 3625 healthy women, we revealed significant effects of age on gut microbiota and age-dependent patterns in microbial features, such as relative abundance, Shannon index, and microbial network properties. Additionally, declining trends in the predictive accuracy of gut microbiota for age groups were shown using iterative sub-sampling based random forest (ISSRF) model. Age-specific species markers were also identified, many of which were shared across age groups. To investigate the influence of age on disease-gut microbiota associations, metagenomic data from 681 women with various disease conditions and 491 matched healthy controls were collected. A substantial proportion of species markers for inflammatory bowel disease (IBD), type 2 diabetes (T2D), atherosclerotic cardiovascular disease (ACVD), and impaired glucose tolerance (IGT) differed in relative abundance across age groups, and were also age-specific species markers. Besides, the microbiota-based probabilities of IBD and ACVD were positively correlated with age. Furthermore, the age specificity of disease-gut microbiota associations was explored using the ISSRF model. Associations between IBD and gut microbiota were age-specific, with reduced stability of disease species markers in childhood and adolescence, possibly due to decrease in the effect size between patients and controls. Our findings provided valuable insights into promoting healthy aging and developing personalized healthcare strategies for women.

Microbial dysbiosis index for assessing colitis status in mouse models: A systematic review and meta-analysis

Although countless gut microbiome studies on colitis using mouse models have been carried out, experiments with small sample sizes have encountered reproducibility limitations because of batch effects and statistical errors. In this study, dextran-sodium-sulfate-induced microbial dysbiosis index (DiMDI) was introduced as a reliable dysbiosis index that can be used to assess the state of microbial dysbiosis in DSS-induced mouse models. Meta-analysis of 189 datasets from 11 independent studies was performed to construct the DiMDI. Microbial dysbiosis biomarkers, Muribaculaceae, Alistipes, Turicibacter, and Bacteroides, were selected through four different feature selection methods and used to construct the DiMDI. This index demonstrated a high accuracy of 82.3% and showed strong robustness (88.9%) in the independent cohort. Therefore, DiMDI may be used as a standard for assessing microbial imbalance in DSS-induced mouse models and may contribute to the development of reliable colitis microbiome studies in mouse experiments.

Gut Microbiome and Metabolomics Study of Selenium-Enriched Kiwifruit Regulating Hyperlipidemia in Mice Induced by a High-Fat Diet.

Our previous study showed that as a substitute for statins, selenium-enriched kiwifruit (Se-Kiwi) might reduce blood lipids and protect the liver in Kunming mice, but the underlying mechanism remains unclear. Metabolic regulation of mammalian intestinal microflora plays an important role in obesity and related diseases induced by a high-fat diet (HFD). Here, samples of serum, liver, colon, and fresh feces from the Se-Kiwi-treated hyperlipidemia C57BL/6J mouse model were collected. Based on metabolome (UHPLC-Q-TOF MS) and gut microbiome (16S rDNA) analyses as well as the integrative analysis of physiological and biochemical indices and pathological data of mice, we aimed to systematically illustrate the gut microbiome and metabolomics mechanism of Se-Kiwi in HFD-induced hyperlipidemic mice. As a result, Se-Kiwi can significantly increase the abundance of potentially beneficial gut bacteria such as Parabacteroides, Bacteroides, and Allobaculum in the colon and improve hyperlipidemia by regulating the digestion and absorption of vitamins, pyrimidine metabolism, purine metabolism, and other metabolic pathways, which have been confirmed by the following fecal microbiota transplantation experiment. This process was significantly regulated by the Ada, Gda, Pank1, Ppara, Pparg, and Cd36 genes. These findings may provide a theoretical basis for the research and development of selenium-enriched functional foods in the treatment of hyperlipidemia.

Halophilic Plant-Associated Bacteria with Plant-Growth-Promoting Potential.

The salinization of soils is a growing agricultural concern worldwide. Irrigation practices, drought, and climate change are leading to elevated salinity levels in many regions, resulting in reduced crop yields. However, there is potential for a solution in the microbiome of halophytes, which are naturally salt-tolerant plants. These plants harbor a salt-tolerant microbiome in their rhizosphere (around roots) and endosphere (within plant tissue). These bacteria may play a significant role in conferring salt tolerance to the host plants. This leads to the possibility of transferring these beneficial bacteria, known as salt-tolerant plant-growth-promoting bacteria (ST-PGPB), to salt-sensitive plants, enabling them to grow in salt-affected areas to improve crop productivity. In this review, the background of salt-tolerant microbiomes is discussed and their potential use as ST-PGPB inocula is explored. We focus on two Gram-negative bacterial genera, Halomonas and Kushneria, which are commonly found in highly saline environments. These genera have been found to be associated with some halophytes, suggesting their potential for facilitating ST-PGPB activity. The study of salt-tolerant microbiomes and their use as PGPB holds promise for addressing the challenges posed by soil salinity in the context of efforts to improve crop growth in salt-affected areas.

Phylogenetic association analysis with conditional rank correlation.

Phylogenetic association analysis plays a crucial role in investigating the correlation between microbial compositions and specific outcomes of interest in microbiome studies. However, existing methods for testing such associations have limitations related to the assumption of a linear association in high-dimensional settings and the handling of confounding effects. Hence, there is a need for methods capable of characterizing complex associations, including nonmonotonic relationships. This article introduces a novel phylogenetic association analysis framework and associated tests to address these challenges by employing conditional rank correlation as a measure of association. The proposed tests account for confounders in a fully nonparametric manner, ensuring robustness against outliers and the ability to detect diverse dependencies. The proposed framework aggregates conditional rank correlations for subtrees using weighted sum and maximum approaches to capture both dense and sparse signals. The significance level of the test statistics is determined by calibration through a nearest-neighbour bootstrapping method, which is straightforward to implement and can accommodate additional datasets when these are available. The practical advantages of the proposed framework are demonstrated through numerical experiments using both simulated and real microbiome datasets.

Possible role of gut derived metabolites in communication between systemic inflammation and CNS in delirium development.

AbstractBackgroundDementia and delirium are strongly correlated. Patients with dementia are more prone to develop delirium, while delirium can lead to accelerated cognitive decline. Biomarkers present in the CSF have been considered to belong to the pathophysiological pathways between systemic inflammation, neuroinflammation, gut microbiota and consequently, contribute to the etiology of both the cognition impairments, ie. delirium and dementia. Despite multiple studies on inflammatory biomarkers in CSF, the link with systemic inflammation has still not been deciphered. Multiple studies have indicated that inflammatory pathways in patients with cognitive decline are mediated by gut microbiota.MethodIn this narrative review we screened PubMed for studies, where neuroinflammatory biomarkers detected in CSF were investigated in relation to delirium. We will explain the link to microbiome by comparing these delirium studies to microbiome studies in dementia. These studies were published in English till January 2023.ResultWe found that in patients with Alzheimer’s disease (AD) and delirium altered levels of at least 70 different neuroinflammatory biomarkers in CSF were found. Also altered microbiome has been identified in AD and its correlation with the CNS inflammation was shown. Interestingly, in case of AD, a recent study demonstrated that a microbial‐derived metabolite, trimethylamine N‐oxide (TMAO), reaches the CNS and that its elevated levels are present in AD patients.In case of delirium, we found two studies indicating contribution of disbalanced gut microbiota in acute CNS inflammation in human. It has been shown that preoperative status of gut microbiota can allow prediction of postoperative delirium in patients with gastric cancer. Interestingly, there was already one case described where fecal microbiota transplantation ameliorated patient’s infection and delirium. No studies on microbiome‐derived metabolites, which could constitute a possible link with the acute inflammation of CNS in delirium, have been found.ConclusionThere is irrefutable evidence that altered levels of neuroinflammatory biomarkers in CSF are associated with pathology of both AD and delirium. Implication of the microbiome, and microbial‐derived metabolite TMAO in inflammatory processes and in pathophysiology of AD has already been found. This has encouraged us to investigate the role of TMAO in the acute CNS inflammatory condition, i.e. delirium.

Molecular ecology of microbiomes in the wild: Common pitfalls, methodological advances and future directions.

The study of microbiomes across organisms and environments has become a prominent focus in molecular ecology. This perspective article explores common challenges, methodological advancements, and future directions in the field. Key research areas include understanding the drivers of microbiome community assembly, linking microbiome composition to host genetics, exploring microbial functions, transience and spatial partitioning, and disentangling non-bacterial components of the microbiome. Methodological advancements, such as quantifying absolute abundances, sequencing complete genomes, and utilizing novel statistical approaches, are also useful tools for understanding complex microbial diversity patterns. Our aims are to encourage robust practices in microbiome studies and inspire researchers to explore the next frontier of this rapidly changing field.

Exercise and the gut microbiome: implications for supportive care in cancer.

Growing recognition of the gut microbiome as an influential modulator of cancer treatment efficacy and toxicity has led to the emergence of clinical interventions targeting the microbiome to enhance cancer and health outcomes. The highly modifiable nature of microbiota to endogenous, exogenous, and environmental inputs enables interventions to promote resilience of the gut microbiome that have rapid effects on host health, or response to cancer treatment. While diet, probiotics, and faecal microbiota transplant are primary avenues of therapy focused on restoring or protecting gut function in people undergoing cancer treatment, the role of physical activity and exercise has scarcely been examined in this population. A narrative review was conducted to explore the nexus between cancer care and the gut microbiome in the context of physical activity and exercise as a widely available and clinically effective supportive care strategy used by cancer survivors. Exercise can facilitate a more diverse gut microbiome and functional metabolome in humans; however, most physical activity and exercise studies have been conducted in healthy or athletic populations, primarily using aerobic exercise modalities. A scarcity of exercise and microbiome studies in cancer exists. Exercise remains an attractive avenue to promote microbiome health in cancer survivors. Future research should elucidate the various influences of exercise modalities, intensities, frequencies, durations, and volumes to explore dose-response relationships between exercise and the gut microbiome among cancer survivors, as well as multifaceted approaches (such as diet and probiotics), and examine the influences of exercise on the gut microbiome and associated symptom burden prior to, during, and following cancer treatment.

Increased Plasma Branched Short-Chain Fatty Acids and Improved Glucose Homeostasis: The Microbiome and Insulin Longitudinal Evaluation Study (MILES).

Short chain fatty acids (SCFA) have been extensively studied for potential beneficial roles in glucose homeostasis and risk of diabetes; however, most of this research has focused on butyrate, acetate, and propionate. The effect on metabolism of branched short chain fatty acids (BSCFA), isobutyrate, isovalerate, and methylbutyrate, is largely unknown. In a cohort of 219 non-Hispanic Whites and 126 African Americans, we examined the association of BSCFA with dysglycemia (prediabetes and diabetes) and oral glucose tolerance test-based measures of glucose and insulin homeostasis, as well as with demographic, anthropometric, lifestyle, lipid traits, and other SCFA. We observed a bimodal distribution of BSCFA, with 25 individuals having high levels (HBSCFA group) and 320 individuals having lower levels (L-BSCFA group). The prevalence of dysglycemia was lower in the H-BSCFA group compared to the L-BSCFA group (16% versus 49%, P=0.0014). This association remained significant after adjustment for age, sex, race, BMI, and levels of other SCFA. Consistent with the lower rate of dysglycemia, fasting and postprandial glucose were lower and disposition index was higher in the H-BSCFA group. Additional findings in H-BSCFA versus L-BSCFA included lower fasting and postprandial Cpeptide levels and lower insulin clearance without differences in insulin levels, insulin sensitivity, insulin secretion, or other variables examined, including diet and physical activity. As one of the first human studies associating higher BSCFA levels with lower odds of dysglycemia and improved glucose homeostasis, this study sets the stage for further investigation of BSCFA as a novel target for prevention or treatment of diabetes.

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

MiTree: A Unified Web Cloud Analytic Platform for User-Friendly and Interpretable Microbiome Data Mining Using Tree-Based Methods.

The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human microbiome. Machine learning is a promising approach to help such an effort, especially due to the high complexity of microbiome data. However, many of the current machine learning algorithms are in a "black box", i.e., they are difficult to understand and interpret. In addition, clinicians, public health practitioners and biologists are not usually skilled at computer programming, and they do not always have high-end computing devices. Thus, in this study, we introduce a unified web cloud analytic platform, named MiTree, for user-friendly and interpretable microbiome data mining. MiTree employs tree-based learning methods, including decision tree, random forest and gradient boosting, that are well understood and suited to human microbiome studies. We also stress that MiTree can address both classification and regression problems through covariate-adjusted or unadjusted analysis. MiTree should serve as an easy-to-use and interpretable data mining tool for microbiome-based disease prediction modeling, and should provide new insights into microbiome-based diagnostics, treatment and prevention. MiTree is an open-source software that is available on our web server.

Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach

BackgroundMicrobiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism’s digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured poultry flocks (poultry in this entire work solely refers to chickens) from southeastern U.S. farms was used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter and Listeria) at different stages of poultry growth (START (2–4 weeks old), MID (5–7 weeks old), END (8–11 weeks old)), association with farm management practices and physicochemical properties.ResultWhile we did not see any significant associations between known probiotics and Salmonella or Listeria, we observed significant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at the mid-time point of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-time points of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on the pasture, age at which chickens are introduced to the pasture, diet composition and presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence.ConclusionIntegration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of beneficial probiotics to reduce pathogen prevalence identified here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study findings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides and Faecalibacterium as potential probiotics.

Saponin treatment for eukaryotic DNA depletion alters the microbial DNA profiles by reducing the abundance of Gram-negative bacteria in metagenomics analyses.

Background: Recent advances in microbiome sequencing techniques have provided new insights into the role of the microbiome on human health with potential diagnostic implications. However, these developments are often hampered by the presence of a large amount of human DNA interfering with the analysis of the bacterial content. Nowadays, extensive scientific literature focuses on eukaryotic DNA depletion methods, which successfully remove host DNA in microbiome studies, even if a precise assessment of the impact on bacterial DNA is often missing. Methods: Here, we have investigated a saponin-based DNA isolation protocol commonly applied to different biological matrices to deplete the released host DNA. Results: The bacterial DNA obtained was used to assess the relative abundance of bacterial and human DNA, revealing that the inclusion of 2.5% wt/vol saponin allowed the depletion of most of the host's DNA in favor of bacterial DNA enrichment. However, shotgun metagenomic sequencing showed inaccurate microbial profiles of the DNA samples, highlighting an erroneous increase in Gram-positive DNA. Even the application of 0.0125% wt/vol saponin altered the bacterial profile by depleting Gram-negative bacteria, resulting in an overall increase of Gram-positive bacterial DNA. Conclusion: The application of the saponin-based protocol drastically changes the detection of the microbial composition of human-related biological specimens. In this context, we revealed that saponin targets not only host cells but also specific bacterial cells, thus inducing a drastic reduction in the profiling of Gram-negative bacterial DNA.

Comparative Study of Intestinal Microbiome in Patients with Ulcerative Colitis and Healthy Controls in Korea.

Ulcerative colitis (UC) poses a contemporary medical challenge, with its exact cause still eluding researchers. This is due to various factors, such as the rising incidence, diagnostic complexities, and difficulties associated with its management. We compared the intestinal microbiome of patients with UC to that of healthy controls to determine the qualitative and quantitative changes associated with UC that occur in the intestinal microbiota. The intestinal bacterial abundance in 40 Korean patients with UC and 25 healthy controls was assayed using via next-generation sequencing. There were five major phyla in both groups: Firmicutes (UC patients: 51.12%; healthy controls: 46.90%), Bacteroidota (UC patients: 37.04%; healthy controls: 40.34%), Proteobacteria (UC patients: 6.01%; healthy controls: 11.05%), Actinobacteriota (UC patients: 5.71%; healthy controls: 1.56%), and Desulfobacteriota (UC patients: 0.13%; healthy controls: 0.14%). Firmicutes was more prevalent in patients with UC (51.12%) compared to that of healthy controls (46.90%). Otherwise, Bacteroidota was more prevalent in healthy controls (40.34%) compared to patients with UC (37.04%). Although there was no significant difference, our results showed a substantially lower gut microbiome diversity in patients with UC (mean: 16.5; 95% confidence interval (CI) = 14.956-18.044) than in healthy controls (mean: 17.84; 95% CI = 15.989-19.691), the beta diversity and the flora structure of the microbiome in patients with UC differed from those in healthy controls. This will be helpful for the development of new treatment options and lay the groundwork for future research on UC. To understand the disease mechanism, it is essential to define the different types of microbes in the guts of patients with UC.

Microbiome study of a coupled aquaponic system: unveiling the independency of bacterial communities and their beneficial influences among different compartments

To understand the microbiome composition and interplay among bacterial communities in different compartments of a coupled freshwater aquaponics system growing flathead grey mullet (Mugil cephalus) and lettuces (Lactuca sativa), 16S rRNA gene amplicon sequencing of the V3–V4 region was analysed from each compartment (fish intestine, water from the sedimentation tank, bioballs from the biological filter, water and biofilm from the hydroponic unit, and lettuce roots). The bacterial communities of each sample group showed a stable diversity during all the trial, except for the fish gut microbiota, which displayed lower alpha diversity values. Regarding beta diversity, the structure of bacterial communities belonging to the biofilm adhering to the hydroponic tank walls, bioballs, and lettuce roots resembled each other (weighted and unweighted UniFrac distances), while bacteria from water samples also clustered together. However, both of the above-mentioned bacterial communities did not resemble those of fish gut. We found a low or almost null number of shared Amplicon Sequence Variants (ASVs) among sampled groups which indicated that each compartment worked as an independent microbiome. Regarding fish health and food safety, the microbiome profile did not reveal neither fish pathogens nor bacterial species potentially pathogenic for food health, highlighting the safety of this sustainable food production system.