BMC Microbiology

The gut microbiota is a key player in energy balance, impacting both digestion efficiency and the production of metabolites involved in metabolism. Its composition is highly adaptable, especially in response to diet. Changes in human diet and lifestyle over time - from active, fibre-rich diets to sedentary habits with calorie-dense foods - have likely contributed to the rise in metabolic diseases. Rodent models are widely used to study the links between diet, microbiota and metabolism. However, they have important limitations (e.g. artificial environments, uniform diets and biological differences from humans) which can affect the translation of findings to humans. While mice and humans differ in their microbiota species, they do share some functional similarities. The grey mouse lemur (Microcebus murinus) has been proposed as a promising alternative model. This small primate experiences strong seasonal changes in food availability, leading to distinct physiological states (energy-saving in winter vs active in summer), even in captivity. It is increasingly recognized as a valuable model for biomedical research, supported by recent genomic and molecular advances. However, its gut microbiota has not yet been the subject of study. Consequently, the present study focuses on investigating the gut microbiota of the grey mouse lemur, with a particular emphasis on how these microbiota vary under different dietary regimens. The microbiota of animals fed the standard colony diet was dominated by Prevotella, Bifidobacterium, Megamonas, Streptococcus, Megasphaera and Lactococcus, showing an Prevotella driven enterosignature. We showed that switch from a classical control diet to 3 different diets resulted in change on microbiota composition that is associated with functional redundancy. The present work underline the interest of Microcebus murinus as model for diet and lifestyle studies in relationship with metabolic diseases.

Robust human microbiome analysis requires robust reference datasets obtained from a population that presents similar habits to the one we are trying to assess. We reported here the construction of a robust reference dataset of healthy individuals from urban and surrounding rural areas of the Argentine population. We screened 200 volunteers with strict inclusion/exclusion criteria. Volunteers were also screened with routine blood clinical test analysis and a complete metabolome profile from blood and urine to remove outliers before inclusion in the Next Generation Sequencing dataset. Sequencing was done on an Illumina MiSeq using the V3-V4 16S rRNA. Using these data, we performed de novo community structure prediction by applying clustering methodology based on seven distance and dissimilarity metrics and two clustering methods to the reference set. Using this approach, we discovered four different enterotypes in this community structure. We then trained a model for the classification of any new sample into the structure of the reference set. Once the new sample was classified, it was compared to the reference ranges of both the enterotype-specific subset and the whole reference set. Finally, we challenged the robustness of this methodology using samples from two test case volunteers with clinically proven gut dysbiosis in a time-series sampling with dietary interventions. Our results pointed to the need to carefully analyze the results of gut microbiome in the context of enterotype-specific rather than to a whole population dataset.

The gut microbiome is an indispensable component of the human body. Alteration in the gut microbiota composition leads to various diseases such as obesity, Type 2 Diabetes, Inflammatory Bowel Syndrome, and depression. Microbiome-based precision tests offer a futuristic strategy for wellness and longevity. However, this approach is limited by the lack of definition of a healthy microbiome in different populations and accurate disease prediction. In this study, we aimed to capture the healthy gut microbiome for different populations using the xNARA Gut Profile Test kit and in-house built proprietary algorithm and reference databases. We found that gut microbiome of different populations from India, UAE, and Singapore varied significantly, indicating a distinct geographic gut microbiome signature and Gut Health Index. The gut microbial diversity as measured by the Shannon index revealed UAE had significantly greater alpha diversity than India and Singapore. Prevotella copri (19.27%), Faecalibacterium prausnitzii (4.08%) and Levilactobacillus brevis (4.0%) were the predominant species in the Indian gut. Faecalibacterium prausnitzii (8.54%), Blautia obeum (8.10%), and Phocaeicola vulgatus (4.6%) were primarily present in Singapore participants whereas Prevotella copri (14.92%), Blautia obeum (6.09%) and Roseburia intestinalis (5.81%) were present in UAE participants. Beta diversity indicated the gut microbiota of Indian-origin participants in Singapore and UAE clustered with the indigenous inhabitants of Singapore and UAE. This highlights that geographic location has a profound effect on shaping the gut microbiome architecture than ethnicity. Regional diet and lifestyle could be crucial factors responsible for shaping the gut microbiome. The prediction accuracy of the xNARA Gut algorithm ranged from 66.66-100% when matched with the blood reports. Participants agreed with the xNARA disease risk outcomes for metabolic conditions (60%-100%), gastrofitness (62.5%-100%), mental health (50%-100%), skin conditions (50%-100%) and physical fitness (50%-100%). These observations imply the promising role of gut-based personalized diet and probiotic recommendations for lifestyle and wellness management.

Introductory paragraphFor robust DNA-based gut microbiome analysis, all cells in the stool samples need to be lysed. However, no standards have been developed to evaluate a DNA extraction protocols capability of lysing all cells and its sensitivity on detecting microbial structural differences among samples. In this study, we incrementally increased the intensity of mechanical lysis and integrated lysozyme pretreatment to Protocol Q (PQ), which was recommended as the best from 21 protocols1. A new protocol (LPQ) was optimized when DNA yield, Gram-positive bacteria ratio, and beta diversity all reached to a plateau with no further significant changes, indicating the achievement of saturated lysing. LPQ detected significant differences among three groups of fiber-treated human stool samples and identified 64 responsive ASVs, while a commercial kit failed to detect any significant treatment effects and PQ only detected 17 responsive ASVs. Therefore, saturated lysing as defined in this study should be adopted for evaluating microbiome DNA extraction protocols.

Parkinsons Disease (PD) is the fastest growing neurodegenerative disease and manifests as a synucleinopathy with progressive motor and non-motor symptoms. In this study, we investigated the gut-microbial alterations associated with PD in an -synuclein transgenic mouse model (h-Syn) at both early and late stages of the disease. We utilised high-resolution functional metagenomics with ultra-deep sequencing to ensure the identification of low-abundance and novel taxa. The microbial community and metabolic pathways were profiled using Microba community and pathway profiler, respectively. While the microbial alpha-diversity remained unchanged between the h-Syn and WT group across different disease stages, distinct shifts in microbial composition were observed. The h-Syn group form two separate clusters corresponding to early and late stages of the disease, indicating progressive dysbiosis. Gut dysbiosis in the early stages of PD was characterised with an increase in Staphylococcus species and a decrease of Duncaniella and Muribaculum species. A reduction in lactobacillus genera was also observed in PD. Furthermore, microbes associated with SCFA production declined whereas and opportunistic pathogens increased in abundance. These findings provide evidence supporting the hypothesis that microbiota alterations may contribute to the onset and progression of PD, highlighting potential microbial targets for future therapeutic interventions.

Recent studies have examined the relationship between rapid eye movement sleep behavior disorder (RBD), Parkinsons disease (PD), and the gut microbiota, but no consensus exists on the shared and distinct gut microbiota changes. This study aimed to identify consistent and divergent gut microbiota changes across RBD and PD and to evaluate the performance of machine learning (ML) models in distinguishing PD, RBD, and healthy controls (HC). A meta-analysis of four gut microbiota studies involving PD, RBD, and HC groups was conducted, comprising a total of 973 samples (379 PD, 251 RBD, and 343 HC). ML models could differentiate PD from HC (cross study validation (CSV) AUC 0.61 {+/-} 0.06) and RBD from HC (CSV AUC 0.58 {+/-} 0.03). However, distinguishing between PD and RBD was ineffective (CSV AUC 0.51 {+/-} 0.03). ML models distinguished PD and RBD from HC with weak to moderate predictive accuracy but failed to differentiate PD from RBD.

BackgroundWhile COVID-19 spread globally, the role of the gut microbiota in patient outcomes has remained an area of exploration especially in resource limited settings. This study aimed to comprehensively profile the gut microbiome among Ugandan COVID-19 patients and infer potential implications. MethodsNasopharyngeal swabs, stool, clinical and demographic data were collected from COVID-19 confirmed cases at the COVID-19 isolation and treatment centers in Kampala and Entebbe, Uganda, during the first and second waves of the pandemic in Uganda (i.e., 2020 and 2021, respectively). SARS-CoV-2 presence in the swab samples was confirmed by quantitative real-time RT-PCR assays. 16S rRNA metagenomic next-generation sequencing was performed on the DNA extracted from the stool samples, followed by bioinformatics analysis. Machine learning was used to determine microbes that were associated with disease severity. ResultsWe observed varied gut microbial composition between COVID-19 patients and healthy controls. Potentially pathogenic bacteria such as Klebsiella oxytoca, Salmonella enterica and Serratia marcescens had an increased presence in COVID-19 disease states, especially severe cases. Enrichment of opportunistic pathogens, such as Enterococcus species, and depletion of beneficial microbes, like Alphaproteobacteria, was observed between mild and severe cases. Machine learning identified age and microbes such as Ruminococcaceae, Bacilli, Enterobacteriales, porphyromonadaceae, and Prevotella copri as predictive of severity. ConclusionThese findings suggest that the microbiome plays a role in the dynamics of SARS-CoV-2 infection in African patients. The shift in abundance of specific microbes can moderately predict severity of COVID-19 in this population. Their direct or indirect roles in determining severity should be investigated further for potential therapeutic interventions.

BackgroundLiterature has highlighted the gut microbiotas role in metabolic functions, suggesting a potential link between gut microbiota composition and T2DM. The purpose of the study was to identify microbial signatures unique to T2DM patients and non-diabetic individuals, to compare microbial profiles between the two groups and to investigate how gut microbiota may be related to inflammation associated with T2DM. MethodsA cross-sectional study was conducted involving 51 T2DM patients and 99 non-diabetic South African individuals. Faecal samples were collected and analysed using 16S rRNA gene sequencing to characterize the gut microbiota. Blood samples were obtained to perform HbA1c, CRP and ferritin tests. Bioinformatic and statistical analyses were performed to identify differences in microbial composition and diversity between the two groups. ResultsThe gut microbiota in T2DM patients was predominantly composed of Firmicutes (47.7%), Bacteroidota (37.5%), and Proteobacteria (11.4%), while the non-diabetic group showed a slightly different microbial profile with higher Bacteroidota (41.9%) and a notable presence of Actinobacteriota (4.5%). Abundant families in the T2DM group included Bacteroidaceae (22.8%), Prevotellaceae (7.4%), Enterobacteriaceae (7.4%), Erysipelotrichaceae (6.0%) and Lachnospiraceae (5.2%). The non-diabetic group exhibited dominant families such as Lachnospiraceae 26.7%, Prevotellaceae (25.3%), Bacteroidaceae (12.7%), Ruminococcaceae (9.5%) and Oscillospiraceae (3.8%). At the genus level, Bacteroides (22.8%), Escherichia-Shigella (5.0%), Holdemanella (4.8%), Phascolarctobacterium (3.2%) and Blautia (2.8%) were prevalent in the T2DM group, while Prevotella_9 (22.1%), Bacteroides (12.7%), Agathobacter (6.7%), Blautia (6.3%) and Faecalibacterium (5.1%) were dominant in the non-diabetic group. Differential abundance testing revealed 5 phyla, 16 families, and 25 genera that were either enriched/depleted in T2DM patients relative to non-diabetic individuals. The comparison of alpha diversity metrics between the two groups revealed significant differences across all four measures (P < 0.001), with non-diabetic individuals showing higher values than T2DM patients. HbA1c and CRP levels showed correlations with the relative abundance of various gut microbes at various phyla, family, and genus levels, as well as with all alpha diversity metrics. ConclusionThe study revealed distinct differences in gut microbiota composition between T2DM patients and non-diabetic individuals, with T2DM patients showing a higher prevalence of certain phyla, families, and genera linked to metabolic dysregulation. Non-diabetic individuals exhibited greater microbial diversity and beneficial taxa, highlighting a potential protective microbial profile.

BackgroundNew sensitive techniques have revealed a large population of bacteria in the human urinary tract, challenging the perception of the urine of healthy humans being sterile. While the role of this urinary microbiota is unknown, dysbiosis has been linked to disorders like urgency urinary incontinence and interstitial cystitis. When comparing studies it is crucial to account for possible confounders introduced due to methodological differences. Here we investigated whether storage condition or time of collection, had any impact on the urinary microbial composition. ResultsFor comparison of different storage conditions, urine was collected from five healthy adult female donors, and analyzed by 16S rRNA gene sequencing. Using the same methods, the daily or day-to-day variation in urinary microbiota was investigated in nineteen healthy donors, including four women, five men, five girls, and five boys. With the exception of two male adult donors, none of the tested conditions gave rise to significant differences in alpha and beta diversities between individuals. Conclusion: The composition of the urinary microbiota was found to be highly resilient to changes introduced by storage temperature and duration. In addition, we did not observe any intrapersonal daily or day-to-day variations in microbiota composition in women, girls or boys. Together our study supports flexibility in study design, when conducting urinary microbiota studies. Author summaryThe discovery of bacteria native to the urinary tract in healthy people, a location previously believed to be sterile, has prompted research into the clinical potential of these bacteria. However, methodological weaknesses can significantly influence such studies, and thus development of robust techniques for investigating these bacteria are needed. In the present study, we investigated whether differences in storage following collection, could affect the bacterial composition of urine samples. Next, we investigated if this composition exhibited daily or day-to-day variations. Firstly, we found, that the bacterial composition of urine could be maintained by storage at -80 {degrees}C, -20 {degrees}C, or refrigerated at 4 {degrees}C. Secondly, the bacterial composition of urine remained stable over time. Overall, the results of this study provide information important to study design in future investigations into the clinical implications of urinary bacteria.

Irritable Bowel Syndrome (IBS) is a condition that is quite complicated and shares its symptoms with other related diseases, making it difficult to diagnose. In this study, we initially trained machine learning models on individual microbiome datasets and tested their performance on other datasets, observing variability and low precision among them. To mitigate this, we hypothesised that integrating multiple publicly available microbiome datasets will capture a wide spectrum of microbiome variations across different geographies and demographics. Utilizing this integrated dataset, the XGBoost model achieved a mean accuracy of 0.75 with a standard deviation of 0.04 in 10-fold cross-validation, demonstrating its potential for robust IBS prediction. Explainability analysis identified key bacterial taxa influencing predictions, aligning with existing literature. However, the models performance declined significantly when using a leave-one-dataset-out approach, where the model was trained on all but one dataset and tested on the excluded dataset. The results highlight the challenges of generalizing across diverse datasets due to biological and technical variability. These findings present a cautionary tale regarding the integration of datasets and interpretation of results, emphasizing the need for more comprehensive approaches to develop reliable diagnostic tools for IBS.

IntroductionEnd-stage kidney disease (ESKD) is associated with the accumulation of uremic toxins such as urea, p-cresol, and indole, which significantly contribute to systemic complications such as inflammation, oxidative stress, and gut dysbiosis. Probiotics have demonstrated potential in modulating gut microbiota and reducing these toxins. We evaluated the in vitro efficacy of multiple probiotic strains of Lactobacillus, Bifidobacterium, Sterptococcus, Bacillus and others in degrading urea, p-cresol, and indole. MethodsThe probiotic strains were initially trained on toxin media prior to their evaluation of their toxin breakdown efficiency. Additionally microbiological methods using selective Christiansons broth/agar and Stuarts broth/agar were applied to assess urea breakdown. Liquid Chromatography-Mass Spectrometry (LC-MS) was utilized to quantify p-cresol and indole degradation. ResultsThe results indicated that all probiotic strains exhibited significant activity in reducing urea, p-cresol, and indole concentrations in the culture media. In urea breakdown we observed an oscillation urea/ammonia ratio at 24 hr intervals, although a complete elimination of the ammonium byproduct was not feasible. Among all tested probiotics, Lactobacillus species showed the highest efficiency in urea breakdown. Furthermore, the toxin removing efficacy of the probiotics was evaluated in a simulated gut environment using the TNO invitro gut model. ConclusionCumulatively, the findings suggest that probiotics could offer a promising strategy for the reduction of uremic toxins and their associated complications in ESKD patients.

In the present era, emergence of next generation sequencing approaches has revolutionized the field of gut microbiome study. However, the adopted DNA extraction step used in metagenomics experiments and its efficiency may play a critical role in their reproducibility and outcome. In this study, fecal samples from active and non-tuberculosis subjects (ATB/NTB, n=7) were used. Fecal samples of a subgroup of these subjects were subjected to Mechanical enzymatic lysis (MEL) and Phenol: Chloroform: Isoamyl Alcohol (PCIA) methods of DNA extraction and a third-generation sequencing platform i.e. MinION was employed for microbiome profiling. Findings of this study demonstrated that DNA extraction method significantly impacts the DNA yield and microbial diversity. Irrespective of the adopted method of DNA extraction, ATB patients showed altered microbial diversity compared to NTB controls. Also, the fecal microbial diversity details are better captured in samples processed by MEL method and may be suitable to be adopted for high-throughput gut microbiome studies.

The tumor-associated microbiome is a key player in cancer development, progression, prognosis, and therapeutic response. Notably, distinct microbial signatures have been identified across cancer types. Small cell lung carcinoma (SCLC) accounts for approximately 15% of all lung cancer, yet its microbiome remains unclear. Analyzing the bacteriome composition in tissue from ten SCLC cases and in 10 cases of a heterogenous lung pathology group, we found a distinct microbial signature associated with SCLC with significantly lower diversity and higher dissimilarity, characterized by a higher relative abundance of Firmicutes and Bacteroidota, and a markedly different set of dominant genera (Pseudomonas, Streptococcus and Haemophilus) resulting in an increased Proteobacteria-to-Actinobacteria ratio. Unexpectedly, mycobiome analysis comparing pooled samples of these SCLC cases with ten pooled lung adenocarcinoma (LUAD) cases revealed that the fungal genus Taphrina was uniquely represented in SCLC. Strikingly, mycobiome individual analysis of twenty-one additional SCLC cases compared with 10 LUAD cases showed an increased prevalence of Taphrina sp. in SLCL tissue. Overall, the results suggest that SCLC microbiome is distinct from other lung pathologies and uncovers a novel link between the biotrophic plant pathogenic Taphrina and human cancer.

Recent research has elucidated many factors which play a role in the development and composition of human microbiomes. In this study we briefly examine the microbiomes of saliva and fecal samples from 71 indigenous individuals, and chicha samples from 28 single family households in a remote community in the Ecuadorian Amazon. Fecal and saliva samples were collected at two separate time points whereas chicha samples were collected at four time points, once each day of the fermentation process. In total 324 samples were collected: 113 saliva, 108 chicha, and 103 fecal. Microbial composition and diversity were assessed using shotgun metagenome sequence data. Chicha samples were found to be nearly entirely composed of the order Lactobacillales, accounting for 90.1% of the relative abundance. Saliva samples also contained a high relative abundance of Lactobacillales (31.9%) as well as being composed of Neisseriales (12.8%), Actinomycineae (8.7%), Bacteroidales (7.0%), Clostridiales (6.8%), Micrococcineae (6.5%), and Pasteurellales (6.0%). Fecal samples were largely composed of the three orders Clostridiales (33.7%), Bacteroidales (21.9%), and Bifidobacteriales (16.5%). Comparison of -diversity, as calculated by Shannons Diversity Index, in mothers and their offspring showed no significant difference between the two groups in either fecal or saliva samples. Comparison of {beta}-diversity in fecal and saliva samples, as calculated by the Bray-Curtis Dissimilarity measure, within household units and between differing households showed that members of the same household were significantly less dissimilar to each other than to members of other households in the community. Average microbiome composition for individuals within fecal and saliva samples was assessed to determine the impact of an individuals household on the composition of their microbiome. Household was determined to have a significant impact on both fecal and oral microbiome compositions.

The gut microbiome plays a vital role in host homeostasis and understanding of its biology is essential for a better comprehension of the etiology of disorders such as Foetal Alcohol Spectrum Disorder. Foetal Alcohol Spectrum Disorder represents a cluster of abnormalities including growth deficiencies and neurological impairments, which are not easily diagnosed nor treated. Here the effect of ethanol exposure in utero on the gut microbial profiles of 16 infant mice (nine exposed in utero and seven non-exposed) was assessed by targeted nanopore sequencing and Illumina sequencing approaches. The nanopore sequencing was implemented using MinION system targeting PCR-amplified amplicons made from the full-length 16S rRNA gene. The Illumina sequencing was performed using Miseq system targeting the V3-V4 region of the 16S rRNA gene. Ethanol exposure did not affect the microbial profiles. Several low prevalent taxa, like Akkermansia muciniphila, were detected but further studies must be performed to detail the effect of ethanol exposure to these taxa since no clear pattern was detected throughout this study. ImportanceDetailed knowledge about the interactions between gut microbes and the developing nervous system is still scarce. Foetal Alcohol Spectrum Disorder represents a clinically relevant set of conditions with cumbersome diagnostic and treatment. In this work the microbial profiles of infant mice gut exposed to ethanol in utero were analysed through third-generation Illumina and optimized next-generation nanopore sequencing technologies. The fungal (albeit not detected) and bacterial microbial profiles here obtained through nanopore and Illumina sequencing represent a technological and biological advancement towards a better comprehension of the microbial landscape in Foetal Alcohol Spectrum Disorder at early post-natal periods.

Maes et al. (2008) published the first paper demonstrating that major depressive disorder (MDD) is accompanied by abnormalities in the microbiota-gut-brain axis, as evidenced by elevated serum IgM/IgA to lipopolysaccharides (LPS) of Gram-negative bacteria, such as Morganella morganii and Klebsiella Pneumoniae. The latter aberrations, which point to increased gut permeability (leaky gut), are linked to activated neuro-immune and oxidative pathways in MDD. To delineate the profile and composition of the gut microbiome in Thai patients with MDD, we examined fecal samples of 32 MDD patients and 37 controls using 16S rDNA sequencing and analyzed -(Chao and Shannon indices) and {beta}-diversity (Bray-Curtis dissimilarity) and conducted Linear discriminant analysis (LDA) Effect Size (LEfSe) analysis. Neither -nor {beta}-diversity differed significantly between MDD and controls. Rhodospirillaceae, Hungatella, Clostridium bolteae, Hungatella hathewayi, and Clostridium propionicum were significantly enriched in MDD, while Gracillibacteraceae family, Lutispora, and Ruminococcus genus, Ruminococcus callidus, Desulfovibrio piger, Coprococcus comes, and Gemmiger, were enriched in controls. Contradictory results have been reported for all these taxa, with the exception of Ruminococcus which is depleted in 6 different MDD studies (one study showed increased abundance), many medical disorders that show comorbidities with MDD, and animal MDD models. Our results may suggest a specific profile of compositional gut dysbiosis in Thai MDD patients with increases in some pathobionts and depletion of some beneficial microbiota. The results suggest that depletion of Ruminococcus may be a more universal biomarker of MDD that maybe contributes to increased enteral LPS load, LPS translocation, and gut-brain axis abnormalities.

Urinary tract infections (UTIs) are the most prevalent bacterial infections globally, and their management increasingly challenged by antimicrobial resistance (AMR). Probiotics offer a promising approach to mitigate AMR by competitively excluding uropathogens and enhancing host immunity by producing immune modulators. Despite being potential, key gaps persist between the discovery of uroprotective probiotic strains and optimization of formulations for urinary tract delivery. Here, we analyzed the urinary microbiome of UTI patients and healthy individuals to identify potential probiotic candidates for the prevention and management of UTIs. Publicly available 16S rRNA amplicon sequencing data of the urinary tract were processed using a standardized pipeline for sequence quality assessment, taxonomic assignment, and microbial function prediction. Comparative analysis showed a significant shift in microbial composition between UTI patients and healthy controls. The dominated phyla identified included Acidobacteriota, Actinobacteriota, Bacteroidota, Campylobacterota, Cyanobacteria, Firmicutes, Fusobacteriota, Patescibacteria, Proteobacteria, and Synergistota. Overall differential abundance analysis revealed Escherichia coli as the predominant UTI-associated species, while Lactobacillus crispatus was enriched in healthy samples. Additionally, predictive functional analysis indicated that metabolic pathways associated with beneficial microbes were enriched in the healthy group. Overall, the study highlights the association of distinct urinary microbiome signatures with infection status, which supports L. crispatus as the most promising probiotic for UTI prevention and control.

The Escherichia coli Nissle 1917 strain (EcN) has shown its probiotic efficacy against many enteric pathogenic bacteria infecting human, including Vibrio cholerae, either alone or in combination with prebiotics. Understanding of these mechanisms of infection control requires the basic knowledge of probiotic mediated gut microbial community alterations especially in presence of different prebiotics. The present study has used the ex-vivo microbiota model and Next Generation Sequencing techniques to demonstrate the effect of EcN along with different sugars, namely glucose, galactose and starch, on the human gut microbiome community composition. The microbiome compositional changes have been observed at two different time-points, set one and a half years apart, in fecal slurries obtained from two donors. The study has indicated that the extent of microbiome alterations varies with different carbohydrate prebiotics and EcN probiotic and most of the alterations are broadly dependent upon the existing gut microbial community structure of the donors. The major distinct compositional changes have been found in the conditions where glucose and starch were administered, both with and without EcN, in spite of the inter-donor microbial community variation. Several of these microbiome component variations also remain consistent for both the time-points, including genus like Bacteroides, Prevotella and Lactobacillus. Altogether, the present study has shown the effectiveness of EcN along with glucose and starch towards specific changes of microbial community alterations independent of initial microbial composition. This type of model study can be implemented for hypothesis testing in case of therapeutic and prophylactic use of probiotic and prebiotic combinations.

IntroductionUrothelial carcinoma (UC) is the tenth most diagnosed cancer in humans worldwide. Dogs are a robust model for invasive UC as tumor development and progression is similar in humans and dogs. Recent studies on urine microbiota in humans revealed alterations in microbial diversity and composition in individuals with UC; however, the potential role of microbiota in UC has yet to be elucidated. Dogs could be valuable models for this research, but microbial alterations in dogs with UC have not been evaluated. ObjectiveThe objective of this this pilot study was to compare the urine and fecal microbiota of dogs with UC (n = 7) and age-, sex-, and breed-matched healthy controls (n = 7). MethodsDNA was extracted from mid-stream free-catch urine and fecal samples using Qiagen Bacteremia and PowerFecal kits, respectively. 16S rRNA gene sequencing was performed followed by sequence processing and analyses (QIIME 2 and R). ResultsCanine urine and fecal samples were dominated by taxa similar to those found in humans. Significantly decreased microbial diversity (Kruskal-Wallis: Shannon, p = 0.048) and altered bacterial composition were observed in the urine but not feces of dogs with UC (PERMANOVA: Unweighted UniFrac, p = 0.011). The relative abundances of Fusobacterium was also increased, although not significantly, in the urine and feces of dogs with UC. ConclusionThis study characterizes urine and fecal microbiota in dogs with UC, and it provides a foundation for future work exploring host-microbe dynamics in UC carcinogenesis, prognosis, and treatment.

IntroductionThe gut microbiota plays a crucial role in various physiological processes, and its composition can be influenced by factors such as diet, genetics, age, and health status. Among these, diet is one of the most significant determinants of microbial balance. A well-balanced diet promotes the growth of beneficial bacteria, reducing intestinal inflammation and the risk of chronic diseases. Vegetarian diets have been proposed to confer beneficial effects on gut health; however, many questions remain regarding microbial profiles, their variability, and the potential influence of geographic and cultural factors--particularly within Latin American contexts. This study aims to address this gap by presenting the first characterization of the gut microbiota in vegetarian adults from the Adventist population living in three distinct regions of Peru. MethodsStool samples were used as a proxy to analyze gut microbiota composition through 16S rRNA gene sequencing. Standard descriptive analyses were performed to assess bacterial composition, including diversity and relative abundance across samples. ResultsThe gut microbial communities of Peruvian vegetarians revealed three distinct enterotypes, with distribution varying by region. Enterotype 1 (ET1), predominant in coastal and highland regions, exhibited the highest bacterial richness and diversity. Enterotype 2 (ET2), observed in highland and jungle areas, was characterized by higher levels of Prevotella. Enterotype 3 (ET3), more frequent in the jungle region, showed a greater abundance of Bacteroides and Faecalibacterium. ConclusionsDespite all participants adhering to a vegetarian diet, notable diversity in gut microbiota profiles was observed within this population. While three distinct enterotypes were identified, consistent with findings in other populations, the specific profiles differed from those previously reported. This study highlights the importance of incorporating variables that enable greater resolution in future research, allowing better control of within-population variability, such as that observed in Peruvian vegetarians, and ultimately enhancing the accuracy of microbiome-related conclusions.