3D printed rectal swabs for assessing the gut microbiome, metabolome and inflammation.
Ontology highlight
ABSTRACT:
SUBMITTER: Perry RW
PROVIDER: S-EPMC11258137 | biostudies-literature | 2024 Jul
REPOSITORIES: biostudies-literature
Publications
3D printed rectal swabs for assessing the gut microbiome, metabolome and inflammation.
Perry Robert W RW Mullish Benjamin H BH Alexander James L JL Shah Raashi R Danckert Nathan P NP Blanco Jesus Miguens JM Roberts Lauren L Liu Zhigang Z Chrysostomou Despoina D Radhakrishnan Shiva T ST Balarajah Sharmili S Barry Rachael R Hicks Lucy C LC Williams Horace R T HRT Marchesi Julian R JR
Scientific reports 20240718 1
Investigating the gut microbiome and metabolome frequently requires faecal samples, which can be difficult to obtain. Previous studies have shown that rectal swabs are comparable to faecal samples for analysing gut microbiota composition and key metabolites. In this study, 3D printed rectal swabs were compared with conventional flocked swabs and faecal samples, due to the potential advantages 3D printing as a technique offers for swab production and development. 16S rRNA gene sequencing, qPCR an ...[more]
Similar Datasets
Project description:Swabs for stool sampling
| PRJEB74914 | ENA
Project description:BackgroundThe purpose of this study was to investigate the use of routinely available rectal swabs as a surrogate sample type for testing the gut microbiome and monitoring antibiotic effects on key gut microorganisms, of patients hospitalised in an intensive care unit. A metagenomic whole genome sequencing approach was undertaken to determine the diversity of organisms as well as resistance genes and to compare findings between the two sampling techniques.ResultsNo significant difference was observed in overall diversity between the faeces and rectal swabs and sampling technique was not demonstrated to predict microbial community variation. More human DNA was present in the swabs and some differences were observed only for a select few anaerobes and bacteria also associated with skin and/or the female genitourinary system, possibly reflecting sampling site or technique. Antibiotics and collections at different times of admission were both considered significant influences on microbial community composition alteration. Detection of antibiotic resistance genes between rectal swabs and faeces were overall not significantly different, although some variations were detected with a potential association with the number of human sequence reads in a sample.ConclusionTesting the gut microbiome using standard rectal swab collection techniques currently used for multi-resistant organism screening has been demonstrated to have utility in gut microbiome monitoring in intensive care. The use of information from this article, in terms of methodology as well as near equivalence demonstrated between rectal swabs and faeces will be able to support and potentially facilitate the introduction into clinical practice.
| S-EPMC9004175 | biostudies-literature
Project description:Previously, we demonstrated that rectal swabs provide a legitimate alternative to faecal sampling for the assessment of the intestinal microbiota in young piglets. However, we also reported that mucosa-adhered microbial populations were more represented in rectal swabs compared to faecal samples, albeit to a degree that varied per swab-sample. Here, we explored the possibility to exploit this variable enrichment of adhered populations in the rectal swabs to assess the impact of diet on mucosa-adhered microbiota in pre-weaning piglets. Paired samples of rectal swabs and colon luminal contents were collected from piglets just before weaning during two independent but similarly designed animal experiments [n = 28 piglets (experiment 1); n = 16 piglets (experiment 2)], with an early feeding treatment (EF) group that had access to customised fibrous feed in addition to sow's milk and a control (CON) group exclusively reared on sow's milk. The intestinal microbiome composition in rectal swabs and colon samples collected at 29 days of age were subjected to metataxonomic analysis. The results identified the genera Escherichia-Shigella, Anaerococcus, Peptostreptococcus, Enterococcus, Trueperella, Actinomyces, and Peptoniphilus as discriminative taxa enriched in rectal swabs compared to colon. Apart from Escherichia-Shigella (10-11% average relative abundance), most of these mucosa-adhered microbial genera display relatively low abundance. Rectal swab microbiota was found to be more variable, which is likely due to variable enrichment of mucosa-adhered microbes. Although almost exclusively driven by one of the experiments, the post-weaning diarrhoea-associated taxa Escherichia-Shigella, was enriched in CON compared to the EF group, suggesting that early life feeding may suppress post-weaning-diarrhoea-related problems in piglets. Our findings demonstrate that rectal swabs allow the investigation of the mucosa-adhered microbial populations as a function of dietary treatment in piglets. This offers opportunities to further study dietary approaches that suppress the abundance of the post-weaning diarrhoea associated adherent microbes like Escherichia-Shigella. Furthermore, we demonstrate that the paired swab-colon microbiota information (obtained from a subset of animals) can predict the mucosa-adhered populations or "mucosity factor" in rectal swab samples, facilitating the analysis of the adhered microbiota in large animal cohort studies using readily obtainable rectal swabs.
| S-EPMC8902596 | biostudies-literature
Project description:COVID-19 greatly disrupted the global supply chain of nasopharyngeal swabs, and thus new products have come to market with little data to support their use. In this prospective study, 2 new 3D printed nasopharyngeal swab designs were evaluated against the standard, flocked nasopharyngeal swab for the diagnosis of COVID-19. Seventy adult patients (37 COVID-positive and 33 COVID-negative) underwent consecutive diagnostic reverse transcription polymerase chain reaction testing, with a flocked swab followed by one or two 3D printed swabs. The "Lattice Swab" (manufacturer Resolution Medical) demonstrated 93.3% sensitivity (95% CI, 77.9%-99.2%) and 96.8% specificity (83.3%-99.9%), yielding κ = 0.90 (0.85-0.96). The "Origin KXG" (manufacturer Origin Laboratories) demonstrated 83.9% sensitivity (66.3%-94.6%) and 100% specificity (88.8%-100.0%), yielding κ = 0.84 (0.77-0.91). Both 3D printed nasopharyngeal swab results have high concordance with the control swab results. The decision to use 3D printed nasopharyngeal swabs during the COVID-19 pandemic should be strongly considered by clinical and research laboratories.
| S-EPMC7577894 | biostudies-literature
Project description:BackgroundThe effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.ResultsWe propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).ConclusionThese results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
| S-EPMC10858481 | biostudies-literature
Project description:Shotgun metagenomics studies have improved our understanding of microbial population dynamics and have revealed significant contributions of microbes to gut homeostasis. They also allow in silico inference of the metagenome. While they link the microbiome with metabolic abnormalities associated with disease phenotypes, they do not capture microbial gene expression patterns that occur in response to the multitude of stimuli that constantly ambush the gut environment. Metatranscriptomics closes that gap, but its implementation is more expensive and tedious. We assessed the metabolic perturbations associated with gut inflammation using shotgun metagenomics and metatranscriptomics. Shotgun metagenomics detected changes in abundance of bacterial taxa known to be SCFA producers, which favors gut homeostasis. Bacteria in the phylum Firmicutes were found at decreased abundance, while those in phyla Bacteroidetes and Proteobacteria were found at increased abundance. Surprisingly, inferring the coding capacity of the microbiome from shotgun metagenomics data did not result in any statistically significant difference, suggesting functional redundancy in the microbiome or poor resolution of shotgun metagenomics data to profile bacterial pathways, especially when sequencing is not very deep. Obviously, the ability of metatranscriptomics libraries to detect transcripts expressed at basal (or simply low) levels is also dependent on sequencing depth. Nevertheless, metatranscriptomics informed about contrasting roles of bacteria during inflammation. Functions involved in nutrient transport, immune suppression and regulation of tissue damage were dramatically upregulated, perhaps contributed by homeostasis-promoting bacteria. Functions ostensibly increasing bacteria pathogenesis were also found upregulated, perhaps as a consequence of increased abundance of Proteobacteria. Bacterial protein synthesis appeared downregulated. In summary, shotgun metagenomics was useful to profile bacterial population composition and taxa relative abundance, but did not inform about differential gene content associated with inflammation. Metatranscriptomics was more robust for capturing bacterial metabolism in real time. Although both approaches are complementary, it is often not possible to apply them in parallel. We hope our data will help researchers to decide which approach is more appropriate for the study of different aspects of the microbiome.
| S-EPMC8851394 | biostudies-literature
Project description:Colorectal cancer (CRC) is a common malignancy worldwide, and the gut microbiota and metabolites play an important role in its initiation and progression. In this study, we constructed a mouse model of inflammation-induced colorectal tumors, with fixed doses of azoxymethane/dextran sulfate sodium (AOM/DSS). We found that colorectal tumors only formed in some mice treated with certain concentrations of AOM/DSS (tumor group), whereas other mice did not develop tumors (non-tumor group). 16S rDNA amplicon sequencing and liquid chromatography-mass spectrometry (LC-MS)/MS analyses were performed to investigate the microbes and metabolites in the fecal samples. As a result, 1189 operational taxonomic units (OTUs) were obtained from the fecal samples, and the non-tumor group had a relatively higher OTU richness and diversity. Moreover, 53 different microbes were identified at the phylum and genus levels, including Proteobacteria, Cyanobacteria, and Prevotella. Furthermore, four bacterial taxa were obviously enriched in the non-tumor group, according to linear discriminant analysis scores (log10) > 4. The untargeted metabolomics analysis revealed significant differences between the fecal samples and metabolic phenotypes. Further, the heatmaps and volcano plots revealed 53 and 19 dysregulated metabolites between the groups, in positive and negative ion modes, respectively. Styrene degradation and amino sugar-nucleotide sugar metabolism pathways were significantly different in positive and negative ion modes, respectively. Moreover, a correlation analysis between the metabolome and microbiome was further conducted, which revealed the key microbiota and metabolites. In conclusion, we successfully established a tumor model using a certain dose of AOM/DSS and identified the differential intestinal microbiota and characteristic metabolites that might modulate tumorigenesis, thereby providing new concepts for the prevention and treatment of CRC.
| S-EPMC9880073 | biostudies-literature
Project description:BackgroundMyopia is one of the most common eye diseases leading to blurred distance vision. Inflammatory diseases could trigger or exacerbate myopic changes. Although gut microbiota bacteria are associated with various inflammatory diseases, little is known about its role in myopia.Materials and methodsThe mice were randomly divided into control and model groups, with the model group being attached-30D lens onto the eyes for 3 weeks. Then, mouse cecal contents and plasma were collected to analyze their intestinal microbiota and plasma metabolome.ResultsWe identified that the microbial composition differed considerably between the myopic and non-myopic mice, with the relative abundance of Firmicutes phylum decreased obviously while that of Actinobacteria phylum was increased in myopia. Furthermore, Actinobacteria and Bifidobacterium were positively correlated with axial lengths (ALs) of eyeballs while negatively correlated with refractive diopters. Untargeted metabolomic analysis identified 141 differentially expressed metabolites, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed considerable enrichment mainly in amino acid metabolism pathways. Notably, pathways involved glutamate metabolism including "Glutamine and D-glutamate metabolism" and "Alanine, aspartate and glutamate metabolism" was changed dramatically, which presented as the concentrations of L-Glutamate and L-Glutamine decreased obviously in myopia. Interestingly, microbiome dysbiosis and metabolites alternations in myopia have a disrupting gut barrier feature. We further demonstrated that the gut barrier function was impaired in myopic mice manifesting in decreased expression of Occludin, ZO-1 and increased permeation of FITC-dextran.DiscussionMyopic mice had obviously altered gut microbiome and metabolites profiles compared to non-myopic mice. The dysbiosis and plasma metabolomics shift in myopia had an interrupting gut barrier feature. Our study provides new insights into the possible role of the gut microbiota in myopia and reinforces the potential feasibility of microbiome-based therapies in myopia.
| S-EPMC10764480 | biostudies-literature
Project description:ObjectivesLarge-scale clinical studies investigating associations between intestinal microbiota signatures and human diseases usually rely on stool samples. However, the timing of repeated stool sample collection cannot be predefined in longitudinal settings. Rectal swabs, being straightforward to obtain, have the potential to overcome this drawback. Therefore, we assessed the usability of rectal swabs for microbiome sampling in a cohort of hematological and oncological patients.Study designWe used a pipeline for intestinal microbiota analysis from deep rectal swabs which was established and validated with test samples and negative controls. Consecutively, a cohort of patients from hematology and oncology wards was established and weekly deep rectal swabs taken during their admissions and re-admissions.ResultsValidation of our newly developed pipeline for intestinal microbiota analysis from rectal swabs revealed consistent and reproducible results. Over a period of nine months, 418 rectal swabs were collected longitudinally from 41 patients. Adherence to the intended sampling protocol was 97%. After DNA extraction, sequencing, read pre-processing and filtering of chimeric sequences, 405 of 418 samples (96.9%) were eligible for further analyses. Follow-up samples and those taken under current antibiotic exposure showed a significant decrease in alpha diversity as compared to baseline samples. Microbial domination occurred most frequently by Enterococcaceae (99 samples, 24.4%) on family level and Enterococcus (90 samples, 22.2%) on genus level. Furthermore, we noticed a high abundance of potential skin commensals in 99 samples (24.4%).SummaryDeep rectal swabs were shown to be reliable for microbiome sampling and analysis, with practical advantages related to high sampling adherence, easy timing, transport and storage. The relatively high abundance of putative skin commensals in this patient cohort may be of potential interest and should be further investigated. Generally, previous findings on alpha diversity dynamics obtained from stool samples were confirmed.
| S-EPMC6464231 | biostudies-literature
Project description:BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.
| S-EPMC8400760 | biostudies-literature