BMC Microbiology

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.

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.

BackgroundBarcoding of isogenic strains is a powerful approach to assess pathogen population dynamics during infection. Here, we adapted WISH-barcoding to Salmonella Typhimurium ATCC14028s to evaluate its suitability for pooled infection experiments in streptomycin-pretreated mouse models. ResultsWISH-barcoded wild-type pools showed pronounced population instability, characterized by stochastic strain loss and segregation into high- and low-fitness subpopulations. Whole-genome sequencing identified recurrent mutations in the methyltransferase rsmG and loss of the P3 plasmid carrying streptomycin resistance in low-fitness strains; neither was observed in {Delta}invG or {Delta}ssaV pools. We propose that rsmG mutations were enriched during strain construction carried out under streptomycin selection, following loss of the P3 plasmid. Control experiments demonstrated that rsmG mutations and P3 loss are counter-selected in vivo and attenuate gut-luminal colonization in streptomycin-pretreated mice. ConclusionWhile population dynamics experiments with ATCC14028s are feasible in principle, wild-type strains are prone to acquiring fitness-altering mutations during in vitro construction when using the P3 plasmid and streptomycin, highlighting the need for careful pool validation prior to use.

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.

This pilot study assessed the composition and diversity of the urinary microbiome from clinically confirmed UTI samples using 16S rRNA sequencing, whilst also exploring inter-individual variability of microbial community structure. We examined ten urine samples from patients with culture-positive UTIs. Demographic and clinical metadata, including age, sex, body mass index (BMI), diabetes status and recent antibiotic exposure was recorded per sample. Metagenomic DNA was extracted from microbial samples and sequenced to generate genus-level taxonomic profiling through 16S rRNA gene sequencing. Relative abundance tables were generated for each of the samples to identify dominant bacterial genera within each sample and summarize cohort level microbial patterns. To evaluate within-sample richness and evenness, alpha diversity indices (Shannon, Simpson, observed features and Chao1) were computed; beta diversity was measured using Bray-Curtis dissimilarity with principal coordinates analysis (PCoA) for graphical representation. The studys findings revealed the sex and moderate clinical diversity of the study sample; all samples were confirmed as having been taken from a UTI patient and exhibited a wide level of heterogeneity regarding the microbial composition of each urine sample. Overall, Pseudomonas was the dominant genus present, however, specific samples had approximately 50% of their microbiomes composed of Klebsiella, Proteus, and Escherichia species as well as approximately 25% of their total microbes were made up of Burkholderia spp., which are closely related to the genus of interest used during the course of this study. The observed alpha diversity of each sample displayed considerable variation for the included samples with a continuum of samples ranging from a single dominant microbe to a highly diverse mixed population producing a highly diverse polymicrobial population/bacterial composition, with some ratios of individual taxa to collective taxa of many samples repeatedly illustrating the exact nature of the specimen. Furthermore, a significant degree of Beta diversity was found between the patients, providing compelling evidence of identifiable differences among urinary microbiomes between patients with UTI. This pilot project provides a clear indication of the diversity and overall heterogeneity of urinary microbiota found in the UTI patients studied. In addition, the results of this study support the notion that the ecological complexities present within a urinary microbiome cannot necessarily be established through conventional culture methods, and that combined with molecular techniques such as 16S rRNA sequencing of bacterial DNA could be used to quantify and characterize the ecologic condition of urinary microbiota separate from the traditional high prevalence of identifiable uropathogens.

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.

Fecal microbiota transplantation (FMT) is an effective therapy for recurrent Clostridioides difficile infection and is increasingly explored for other dysbiosis-related disorders. However, its implementation as a regulated therapeutic strategy still requires robust donor screening, biosafety frameworks, and standardized processing workflows. Here, we describe the establishment of the first fecal microbiota biobank in the south of Brazil and evaluate the incorporation of metagenomic sequencing as a complementary layer of donor safety assessment. A structured donor selection pipeline based on international guidelines was implemented, integrating clinical screening, biochemical and serological testing, and microbiological analyses. Of 100 screened candidates, only four donors met all eligibility criteria and were included in the biobank, highlighting the stringency of the selection process. Shotgun metagenomic sequencing revealed a diverse resistome across all donors, including a shared core set of resistance-related genes alongside marked interindividual variability. Dominant antibiotic resistance genes included tetracycline-associated determinants, as well as ermF, CfxA-type {beta}-lactamases, and aminoglycoside-modifying enzymes, each linked to specific gut taxa. Notably, the relatively high abundance of tetW and ermF in Bacteroides fragilis suggests that this dominant commensal species may act as a reservoir for tetracycline and multidrug resistance determinants within the intestinal microbiota. Rather than serving as exclusion criteria, such determinants highlight the importance of integrating functional genomic profiling into donor characterization. Overall, this study provides a framework for microbiota biobank implementation and supports the use of metagenomics as a complementary strategy to improve biosafety and functional assessment in FMT.

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.

Pseudomonas aeruginosa relies on the redox cofactor pyrroloquinoline quinone (PQQ) for efficient glucose and ethanol metabolism via periplasmic dehydrogenases (Gcd and ExaA). While PQQ biosynthesis is well-characterized, its uptake mechanisms remain unclear. Here, we identify PA2289 (PqqU), a TonB-dependent transporter, as the primary PQQ importer in P. aeruginosa. Growth assays with PQQ-deficient mutants ({Delta}pqqABCDEH) demonstrated that PqqU is essential for exogenous PQQ uptake, rescuing growth on glucose and ethanol. Genomic analysis across 210 P. aeruginosa and 263 Pseudomonas strains revealed high conservation of PQQ biosynthesis and utilization genes, while PqqU showed lower prevalence (47.7%) in the genus. Transcriptional analyses using fluorescent reporters and qRT-PCR demonstrated that PqqU expression remains unchanged in response to PQQ, varying carbon sources, or iron availability, suggesting constitutive regulation. Comparative proteomics between wild-type and {Delta}pqqABCDEH strains, cultured on glucose or ethanol, uncovered extensive proteomic shifts, underscoring P. aeruginosas metabolic adaptability. Additionally, PQQ-dependent metabolic pathways appear to indirectly influence iron homeostasis, most likely through environmental acidification. Together, these results emphasize the critical role of PqqU in PQQ uptake and its broader significance in shaping the metabolic and environmental versatility of Pseudomonas.

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.

BackgroundThe gut microbiome has emerged as a promising non-invasive biomarker for early cancer detection. However, evidence remains fragmented across individual studies with limited cross-cancer comparisons. ObjectivesTo systematically evaluate the diagnostic accuracy of gut microbiome-based signatures across five major cancer types: colorectal cancer (CRC), gastric cancer (GC), pancreatic ductal adenocarcinoma (PDAC), hepatocellular carcinoma (HCC), and lung cancer (LC). MethodsWe conducted a systematic literature search in PubMed, Embase, and Web of Science (January 2000 - April 2026), following PRISMA 2020 guidelines. Studies reporting area under the receiver operating characteristic curve (AUC) for microbiome-based cancer classification were included. Pooled AUC estimates were derived using a DerSimonian-Laird random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). ResultsEighteen studies (2,587 participants) met inclusion criteria. Pooled AUC values were: CRC 0.785 (95%CI 0.750-0.819; I2=30.6%), GC 0.834 (0.781-0.887; I2=56.6%), PDAC 0.853 (0.785-0.921; I2=60.8%), HCC 0.809 (0.747-0.871; I2=70.3%), and LC 0.780 (0.738-0.822; I2=25.0%). Fusobacterium nucleatum was consistently enriched across CRC, GC, and PDAC, while Faecalibacterium prausnitzii and Akkermansia muciniphila were depleted in all five cancer types. Porphyromonas gingivalis showed the highest fold-change in PDAC (log{blacksquare}FC=+2.8). Risk of bias was moderate-to-high in all studies. ConclusionsGut microbiome profiling demonstrates good-to-excellent diagnostic accuracy (AUC 0.78-0.85) across five major cancer types. Shared cross-cancer biomarkers suggest common dysbiotic mechanisms amenable to pan-cancer screening. These findings support integration of microbiome signatures into multi-modal cancer detection platforms.

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.

Background/ObjectivesGlucagon-Like Peptide-1 (GLP-1) is a key incretin hormone that regulates glucose homeostasis and energy metabolism. Impaired GLP-1 signaling contributes to the development of obesity, metabolic syndrome, and type 2 diabetes. Emerging evidence indicates that gut microbiota-derived components can influence GLP-1 secretion, highlighting the therapeutic potential of microbial modulators. Akkermansia muciniphila, a next-generation probiotic associated with improved metabolic health, remains underexplored for its capacity to stimulate GLP-1 release. This study aimed to investigate the GLP-1- stimulatory effects of live and pasteurized (dead) A. muciniphila strains in human enteroendocrine cells. MethodsHuman enteroendocrine L-cells (NCI-H716) were treated with varying doses of live and dead A. muciniphila from Vidya Herbss proprietary VHAKM strain and a commercially available marketed strain (dead form). Following incubation, GLP-1 levels were quantified from culture supernatants using enzyme-linked immunosorbent assay (ELISA). Comparative analyses assessed differences in GLP-1 secretion between strains and treatment forms. ResultsBoth live and pasteurized VHAKM strains significantly increased GLP-1 secretion compared to untreated controls. The live VHAKM strain exhibited higher GLP-1 stimulatory activity than its pasteurized counterpart and the marketed strain. The results suggest a strain-specific and viability-dependent modulation of GLP-1 secretion in human L-cells. ConclusionsThis study demonstrates that A. muciniphila VHAKM enhances GLP-1 secretion in a strain- and form-dependent manner, with live cells showing superior efficacy. These findings provide foundational insights for developing microbiome-targeted interventions to boost endogenous GLP-1 levels and improve metabolic health outcomes.

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.

This study investigates the relationship between the gut microbiota and specific diseases. Data was collected from the Human Gut Microbiome Atlas, which examines regional variations across 20 countries on five continents, categorizing microbial species by taxonomy, from genus to species. The Atlas provides color-coded phylum classifications, numerical species counts within the same genus, and an analysis of dysbiosis-related associations with 23 diseases, as well as region-enriched species. The data stratified samples into distinct categories such as westernized, non-westernized, cancerous, and non-cancerous. The findings demonstrate that tree-based ensemble methods, such as Bagging and Boosting prediction methods, achieved the highest accuracies across all categories due to their robustness in handling the complex, high-dimensional data. The XGBoost model yielded the strongest predictive performance, achieving 91% accuracy for westernized cancer-associated samples, 84% accuracy for non-westernized cancer-associated samples, 92% accuracy for westernized samples, and 78% for non-westernized samples. Additionally, advanced topological data analysis was used to assess the global structure and underlying patterns within the dataset. ImportanceThis research aims to connect gut microbiome composition to diseases using global datasets from the Human Gut Microbiome Atlas. The goal was to evaluate how accurately different machine learning algorithms could classify microbiota species and diseases and predict disease associations by comparing westernized and non-westernized populations, including both cancerous and noncancerous groups. These findings can contribute to the future creation of population-specific and disease-specific microbial models.

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.