Microbiome Research Research Articles

Microbiome modeling: a beginner's guide.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Synthesis of current pediatric urinary microbiome research.

The human urinary bladder hosts a complex microbial community of low biomass referred to as the urobiome. While the composition of the urobiome has been investigated in adults for over a decade now, only a few studies have considered the presence and composition of the urobiome in children. It is critical to explore how the urobiome develops throughout the life span and how it changes in the presence of various health conditions. Therefore, we set to review the available data on pediatric urobiome composition and its development with age and disease. In addition, we focused on identifying and reporting specific gaps in our knowledge of the pediatric urobiome that we hope will be addressed by future studies in this swiftly developing field with fast-improving methods and consensus.

Clinical and microbiological features of patients with irritable bowel syndrome after previous COVID‑19

Objective — to investigate clinical and microbiological features in patients with irritable bowel syndrome (IBS) after coronavirus disease 2019 (COVID‑19). Materials and methods. Examinations involved 68 patients aged 22 to 85 with IBS, including 37 (54.4%) women and 31 (43.6%) men. In 32 patients IBS occurred after COVID‑19 within a year before the study, and the other 36 patients had IBS without a history of COVID‑19. The control group included 30 practically healthy patients without gastrointestinal complaints. All patients were clinically diagnosed with IBS by the Rome criteria IV revision. Fecal samples were taken from each patient for further laboratory testing by a polymerase chain reaction to determine the representatives of obligate and opportunistic microflora. The statistical processing of the study data was performed using the Statistica software. Results. Diarrhea as the leading syndrome was recorded in 81.3% of cases in patients with post‑covid IBS, while patients with IBS without COVID‑19 more often had constipation — in 83.3% of cases. Changes were found in the number of obligate microorganisms during microbiome research: a decrease in the number of Bifidobacterium spp. in the group of patients with post­­COVID‑19 IBS compared to the other two groups, an increase in non‑pathogenic Escherichia coli was also recorded in this group compared to the other two groups, the lowest level of Bacteroides fragilis group was found in patients with non‑post‑COVID‑19 IBS, the highest level was observed in the group of healthy patients. No significant difference was found in the number of pathogenic microflora in all groups. Healthy patients had the greatest diversity of the studied microflora. Conclusions. Patients with irritable bowel syndrome that occurred after COVID‑19 had diarrhea as the most common symptom. The microbiome of such patients showed a decrease in the number of bacteria of the genus Bifidobacterium spp. and an increase in the level of non‑pathogenic Escherichia coli. The number of pathogenic microorganisms did not differ from that in other study groups.

Predicting Microbiome Growth Dynamics under Environmental Perturbations

MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.

Quantifying variation across 16S rRNA gene sequencing runs in human microbiome studies

Recent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs.Key points• Mock community and positive control accuracy were lower than precision.• Samples with lower DNA concentration had increased technical variation across sequencing runs.• Biological variation was significantly higher than technical variation due to sequencing runs.

Investigating the effects of radiation, T cell depletion, and bone marrow transplantation on murine gut microbiota.

Microbiome research has gained much attention in recent years as the importance of gut microbiota in regulating host health becomes increasingly evident. However, the impact of radiation on the microbiota in the murine bone marrow transplantation model is still poorly understood. In this paper, we present key findings from our study on how radiation, followed by bone marrow transplantation with or without T cell depletion, impacts the microbiota in the ileum and caecum. Our findings show that radiation has different effects on the microbiota of the two intestinal regions, with the caecum showing increased interindividual variation, suggesting an impaired ability of the host to regulate microbial symbionts, consistent with the Anna Karenina principle. Additionally, we observed changes in the ileum composition, including an increase in bacterial taxa that are important modulators of host health, such as Akkermansia and Faecalibaculum. In contrast, radiation in the caecum was associated with an increased abundance of several common commensal taxa in the gut, including Lachnospiraceae and Bacteroides. Finally, we found that high doses of radiation had more substantial effects on the caecal microbiota of the T-cell-depleted group than that of the non-T-cell-depleted group. Overall, our results contribute to a better understanding of the complex relationship between radiation and the gut microbiota in the context of bone marrow transplantation and highlight the importance of considering different intestinal regions when studying microbiome responses to environmental stressors.

The Gut Microbiome in Aging and Ovarian Cancer.

The gut microbiome changes with age and affects regions beyond the gut, including the ovarian cancer tumor microenvironment. In this review summarizing the literature on the gut microbiome in ovarian cancer and in aging, we note trends in the microbiota composition common to both phenomena and trends that are distinctly opposite. Both ovarian cancer and aging are characterized by an increase in proinflammatory bacterial species, particularly those belonging to phylum Proteobacteria and genus Escherichia, and a decrease in short chain fatty acid producers, particularly those in Clostridium cluster XIVa (family Lachnospiraceae) and the Actinobacteria genus Bifidobacterium. However, while beneficial bacteria from family Porphyromonadaceae and genus Akkermansia tend to increase with normal, healthy aging, these bacteria tend to decrease in ovarian cancer, similar to what is observed in obesity or unhealthy aging. We also note a lack in the current literature of research demonstrating causal relationships between the gut microbiome and ovarian cancer outcomes and research on the gut microbiome in ovarian cancer in the context of aging, both of which could lead to improvements to ovarian cancer diagnosis and treatment.

Nexus Between Sarcopenia and Microbiome Research: A Bibliometric Exploration.

Despite over 30 years of microbiome and skeletal muscle research, no quantitative analysis of sarcopenia and the microbiome literature had been conducted. Our bibliometric study examined research status, hotspots, and future trends. We utilized bibliometric techniques to search the Science Citation Index Extended Database on February 27, 2023, using the Bibliometrix package in R to create a map displaying scientific production and subject categories. Collaborative network maps between countries/regions were visualized using Scimago Graphica, while VOSviewer explored collaboration modes among individuals and institutions. We analyzed the top 25 emerging keywords, top co-occurring keyword networks, and co-occurring keyword clusters using CiteSpace. A total of 997 articles were retrieved for sarcopenia and microbiome, of which 633 papers were analyzed. Both the number of publications and total citation frequency had been continuously increasing. The United States had the highest total citation frequency, while China had the highest number of publications. Research on the impact of the microbiome on sarcopenia was in its nascent stage and spans multiple disciplines, including nutrition, microbiology, geriatrics, immunology, endocrinology and metabolism, molecular biology, and sports medicine. The University of Copenhagen contributed the most to the number of publications (n=16), with Tibbett M (n=7) and Hulver MW (n=7) among the top authors. The most published journal was "Nutrients" (n=24). Analysis of keywords and clusters revealed new research hotspots in microbes and sarcopenia, such as malnutrition, dietary fiber, signaling pathways, frailty, and intestinal permeability. Research on the impact of the microbiome on sarcopenia is in its infancy and spans multiple disciplines. Malnutrition, dietary fiber, signaling pathways, frailty, and intestinal microbes are currently research hotspots. Furthermore, the visual atlas analysis of research on microbes and sarcopenia helps to track the knowledge structure in research fields related to sarcopenia and microbes, providing direction for future research.

Conceptual Parallels: Microbiome Research and Ancient Medicine.

The concepts currently operating in much medical microbiome research bear a curious resemblance to an ancient tradition of Western medicine. This tradition, humoral medicine, is concerned with the four humors: yellow and black bile, phlegm, blood. Both humoral medicine and medical microbiome research use notions of imbalance and balance for broad explanations of disease and health. Both traditions also hold that the composition of humors or microbiomes determines bodily as well as mental states. Causality in each system is often conceived teleologically, meaning that humors or microbiomes "function for" the maintenance of the whole. And ultimately, each framework situates the humors or microbiomes in a multilevel interactionist theory that conceptualizes individual health within a broader environmental context. As well as critically assessing the parallels between these systems, this article sketches some explanations of how they may have arisen. The authors also evaluate the implications of these similarities for the future of medical microbiome research and suggest ways in which the field might move forward.

The impact of kit, environment, and sampling contamination on the observed microbiome of bovine milk.

In low-microbial biomass samples such as bovine milk, contaminants can outnumber endogenous bacteria. Because of this, milk microbiome research suffers from a critical knowledge gap, namely, does non-mastitis bovine milk contain a native microbiome? In this study, we sampled external and internal mammary epithelia and stripped and cisternal milk and used numerous negative controls, including air and sampling controls and extraction and library preparation blanks, to identify the potential sources of contamination. Two algorithms were used to mathematically remove contaminants and track the potential movement of microbes among samples. Results suggest that the majority (i.e., >75%) of sequence data generated from bovine milk and mammary epithelium samples represents contaminating DNA. Contaminants in milk samples were primarily sourced from DNA extraction kits and the internal and external skin of the teat, while teat canal and apex samples were mainly contaminated during the sampling process. After decontamination, the milk microbiome displayed a more dispersed, less diverse, and compositionally distinct bacterial profile compared with epithelial samples. Similar microbial compositions were observed between cisternal and stripped milk samples, as well as between teat apex and canal samples. Staphylococcus and Acinetobacter were the predominant genera detected in milk sample sequences, and bacterial culture showed growth of Staphylococcus and Corynebacterium spp. in 50% (7/14) of stripped milk samples and growth of Staphylococcus spp. in 7% (1/14) of cisternal milk samples. Our study suggests that microbiome data generated from milk samples obtained from clinically healthy bovine udders may be heavily biased by contaminants that enter the sample during sample collection and processing workflows.IMPORTANCEObtaining a non-contaminated sample of bovine milk is challenging due to the nature of the sampling environment and the route by which milk is typically extracted from the mammary gland. Furthermore, the very low bacterial biomass of bovine milk exacerbates the impacts of contaminant sequences in downstream analyses, which can lead to severe biases. Our finding showed that bovine milk contains very low bacterial biomass and each contamination event (including sampling procedure and DNA extraction process) introduces bacteria and/or DNA fragments that easily outnumber the native bacterial cells. This finding has important implications for our ability to draw robust conclusions from milk microbiome data, especially if the data have not been subjected to rigorous decontamination procedures. Based on these findings, we strongly urge researchers to include numerous negative controls into their sampling and sample processing workflows and to utilize several complementary methods for identifying potential contaminants within the resulting sequence data. These measures will improve the accuracy, reliability, reproducibility, and interpretability of milk microbiome data and research.

Future proofing a long-term agricultural experiment for decades to come: Relocation and redesign

We took land encroachment for urban development that threatened a 60-year-old field experiment as an opportunity to transport and redesign an entire experiment to more effectively address contemporary challenges. The field experiment comprised long-term pH plots, established for agronomic demonstration in 1961, but used over the years for both applied and fundamental research. We redesigned the experimental layout to add strength to the statistical design through randomisation. Continuation and enrichment of the long-term pH experimental platform lends a unique resource for microbiome research per se. Here we have provided a rationale for why the decision was made to move the soils from the former Woodlands Field pH experiment. Moving soil on the scale of a field experiment requires costs and benefits to be weighed up in that the realisation of the transfer costs and the costs for the ongoing maintenance can outweigh the costs of a new experimental set up or indeed closing the experiment and walking away. It is important to recognise that much of the value is in assets that are not monetary. Considerations include the availability of a site suitable from a biophysical perspective but also considering ownership and future access. The topsoil (0–20 cm to plough depth) was moved to a new location in a similar environment and within the same soil association. The soil was disturbed under very dry conditions and placed back into the earth in the new location within 90 minutes, and thus as near to normal cultivation as possible. Additional plots were added to the experiment that will be amended to the pH treatments in the long-term experiment providing an exciting opportunity to assess how soil microbial communities change over time. Soil samples taken 2 years after the relocation of the soils indicate that the soil pH gradient (4.5–7.5) has been maintained. Safeguarding this long-term resource on soil pH helps us to predict the impacts of changed practice with regard to liming on productivity and also to address wider contemporary and future issues surrounding net zero, food security and soil protection.

Microbiome research in Africa must be based on equitable partnerships.

Microbiome research in Africa must be based on equitable partnerships.

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

Quantitative analysis of the effects of brushing, flossing, and mouthrinsing on supragingival and subgingival plaque microbiota: 12-week clinical trial

BackgroundTranslational microbiome research using next-generation DNA sequencing is challenging due to the semi-qualitative nature of relative abundance data. A novel method for quantitative analysis was applied in this 12-week clinical trial to understand the mechanical vs. chemotherapeutic actions of brushing, flossing, and mouthrinsing against the supragingival dental plaque microbiome. Enumeration of viable bacteria using vPCR was also applied on supragingival plaque for validation and on subgingival plaque to evaluate interventional effects below the gingival margin.MethodsSubjects with gingivitis were enrolled in a single center, examiner-blind, virtually supervised, parallel group controlled clinical trial. Subjects with gingivitis were randomized into brushing only (B); brushing and flossing (BF); brushing and rinsing with Listerine® Cool Mint® Antiseptic (BA); brushing and rinsing with Listerine® Cool Mint® Zero (BZ); or brushing, flossing, and rinsing with Listerine® Cool Mint® Zero (BFZ). All subjects brushed twice daily for 1 min with a sodium monofluorophosphate toothpaste and a soft-bristled toothbrush. Subjects who flossed used unflavored waxed dental floss once daily. Subjects assigned to mouthrinses rinsed twice daily. Plaque specimens were collected at the baseline visit and after 4 and 12 weeks of intervention. Bacterial cell number quantification was achieved by adding reference amounts of DNA controls to plaque samples prior to DNA extraction, followed by shallow shotgun metagenome sequencing.Results286 subjects completed the trial. The metagenomic data for supragingival plaque showed significant reductions in Shannon-Weaver diversity, species richness, and total and categorical bacterial abundances (commensal, gingivitis, and malodor) after 4 and 12 weeks for the BA, BZ, and BFZ groups compared to the B group, while no significant differences were observed between the B and BF groups. Supragingival plaque vPCR further validated these results, and subgingival plaque vPCR demonstrated significant efficacy for the BFZ intervention only.ConclusionsThis publication reports on a successful application of a quantitative method of microbiome analysis in a clinical trial demonstrating the sustained and superior efficacy of essential oil mouthrinses at controlling dental plaque compared to mechanical methods. The quantitative microbiological data in this trial also reinforce the safety and mechanism of action of EO mouthrinses against plaque microbial ecology and highlights the importance of elevating EO mouthrinsing as an integral part of an oral hygiene regimen.Trial registrationThe trial was registered on ClinicalTrials.gov on 31/10/2022. The registration number is NCT05600231.

Identification and validation of microbial biomarkers from cross-cohort datasets using xMarkerFinder.

Microbial signatures have emerged as promising biomarkers for disease diagnostics and prognostics, yet their variability across different studies calls for a standardized approach to biomarker research. Therefore, we introduce xMarkerFinder, a four-stage computational framework for microbial biomarker identification with comprehensive validations from cross-cohort datasets, including differential signature identification, model construction, model validation and biomarker interpretation. xMarkerFinder enables the identification and validation of reproducible biomarkers for cross-cohort studies, along with the establishment of classification models and potential microbiome-induced mechanisms. Originally developed for gut microbiome research, xMarkerFinder's adaptable design makes it applicable to various microbial habitats and data types. Distinct from existing biomarker research tools that typically concentrate on a singular aspect, xMarkerFinder uniquely incorporates a sophisticated feature selection process, specifically designed to address the heterogeneity between different cohorts, extensive internal and external validations, and detailed specificity assessments. Execution time varies depending on the sample size, selected algorithm and computational resource. Accessible via GitHub ( https://github.com/tjcadd2020/xMarkerFinder ), xMarkerFinder supports users with diverse expertise levels through different execution options, including step-to-step scripts with detailed tutorials and frequently asked questions, a single-command execution script, a ready-to-use Docker image and a user-friendly web server ( https://www.biosino.org/xmarkerfinder ).

Evaluation of fecal sample collection methods for feline gut microbiome profiling: fecal loop vs. litter box.

Microbial population structures within fecal samples are vital for disease screening, diagnosis, and gut microbiome research. The two primary methods for collecting feline fecal samples are: (1) using a fecal loop, which retrieves a rectal sample using a small, looped instrument, and (2) using the litter box, which collects stool directly from the litter. Each method has its own advantages and disadvantages and is suitable for different research objectives. Whole-genome shotgun metagenomic sequencing were performed on the gut microbiomes of fecal samples collected using these two methods from 10 adult cats housed in the same research facility. We evaluated the influence of collection methods on feline microbiome analysis, particularly their impact on DNA extraction, metagenomic sequencing yield, microbial composition, and diversity in subsequent gut microbiome analyses. Interestingly, fecal sample collection using a fecal loop resulted in a lower yield of microbial DNA compared to the litterbox method (p = 0.004). However, there were no significant differences between the two groups in the proportion of host contamination (p = 0.106), virus contamination (p = 0.232), relative taxonomy abundance of top five phyla (Padj > 0.638), or the number of microbial genes covered (p = 0.770). Furthermore, no significant differences were observed in alpha-diversity, beta-diversity, the number of taxa identified at each taxonomic level, and the relative abundance of taxonomic units. These two sample collection methods do not affect microbial population structures within fecal samples and collecting fecal samples directly from the litterbox within 6 hours after defecation can be considered a reliable approach for microbiome research.

Root-secreted nucleosides: signaling chemoattractants of rhizosphere bacteria.

The rhizosphere is a complex ecosystem, consisting of a narrow soil zone influenced by plant roots and inhabited by soil-borne microorganisms. Plants actively shape the rhizosphere microbiome through root exudates. Some metabolites are signaling molecules specifically functioning as chemoattractants rather than nutrients. These elusive signaling molecules have been sought for several decades, and yet little progress has been made. Root-secreted nucleosides and deoxynucleosides were detected in exudates of various plants by targeted ultra-performance liquid chromatography-mass spectrometry/mass spectrometry. Rhizobacteria were isolated from the roots of Helianthemum sessiliflorum carrying the mycorrhizal desert truffle Terfezia boudieri. Chemotaxis was determined by a glass capillary assay or plate assays on semisolid agar and through a soil plate assay. Nucleosides were identified in root exudates of plants that inhabit diverse ecological niches. Nucleosides induced positive chemotaxis in plant beneficial bacteria Bacillus pumilus, Bacillus subtilis, Pseudomonas turukhanskensis spp., Serratia marcescens, and the pathogenic rhizobacterium Xanthomonas campestris and E coli. In a soil plate assay, nucleosides diffused to substantial distances and evoked chemotaxis under conditions as close as possible to natural environments. This study implies that root-secreted nucleosides are involved in the assembly of the rhizosphere bacterial community by inducing chemotaxis toward plant roots. In animals, nucleoside secretion known as "purinergic signaling" is involved in communication between cells, physiological processes, diseases, phagocytic cell migration, and bacterial activity. The coliform bacterium E. coli that inhabits the lower intestine of warm-blooded organisms also attracted to nucleosides, implying that nucleosides may serve as a common signal for bacterial species inhabiting distinct habitats. Taken together, all these may indicate that chemotaxis signaling by nucleosides is a conserved universal mechanism that encompasses living kingdoms and environments and should be given further attention in plant rhizosphere microbiome research.

Quantifying fecal and plasma short-chain fatty acids in healthy Thai individuals

Short-chain fatty acids (SCFAs) are involved in important physiological processes such as gut health and immune response, and changes in SCFA levels can be indicative of disease. Despite the importance of SCFAs in human health and disease, reference values for fecal and plasma SCFA concentrations in healthy individuals are scarce. To address this gap in current knowledge, we developed a simple and reliable derivatization-free GC-TOFMS method for quantifying fecal and plasma SCFAs in healthy individuals. We targeted six linear- and seven branched-SCFAs, obtaining method recoveries of 73–88% and 83–134% in fecal and plasma matrices, respectively. The developed methods are simpler, faster, and more sensitive than previously published methods and are well suited for large-scale studies. Analysis of samples from 157 medically confirmed healthy individuals showed that the total SCFAs in the feces and plasma were 34.1 ± 15.3 µmol/g and 60.0 ± 45.9 µM, respectively. In fecal samples, acetic acid (Ace), propionic acid (Pro), and butanoic acid (But) were all significant, collectively accounting for 89% of the total SCFAs, whereas the only major SCFA in plasma samples was Ace, constituting of 93% of the total plasma SCFAs. There were no statistically significant differences in the total fecal and plasma SCFA concentrations between sexes or among age groups. The data revealed, however, a positive correlation for several nutrients, such as carbohydrate, fat, iron from vegetables, and water, to most of the targeted SCFAs. This is the first large-scale study to report SCFA reference intervals in the plasma and feces of healthy individuals, and thereby delivers valuable data for microbiome, metabolomics, and biomarker research.

Dysbiosis Signature of Fecal Microbiota in Patients with Pancreatic Adenocarcinoma and Pancreatic Intraductal Papillary Mucinous Neoplasms.

Pancreatic cancer (PC) ranks as the seventh leading cause of cancer-related deaths, with approximately 500,000 new cases reported in 2020. Existing strategies for early PC detection primarily target individuals at high risk of developing the disease. Nevertheless, there is a pressing need to identify innovative clinical approaches and personalized treatments for effective PC management. This study aimed to explore the dysbiosis signature of the fecal microbiota in PC and potential distinctions between its Intraductal papillary mucinous neoplasm (IPMN) and pancreatic ductal adenocarcinoma (PDAC) phenotypes, which could carry diagnostic significance. The study enrolled 33 participants, including 22 diagnosed with PDAC, 11 with IPMN, and 24 healthy controls. Fecal samples were collected and subjected to microbial diversity analysis across various taxonomic levels. The findings revealed elevated abundances of Firmicutes and Proteobacteria in PC patients, whereas healthy controls exhibited higher proportions of Bacteroidota. Both LEfSe and Random Forest analyses indicated the microbiome's potential to effectively distinguish between PC and healthy control samples but fell short of differentiating between IPMN and PDAC samples. These results contribute to the current understanding of this challenging cancer type and highlight the applications of microbiome research. In essence, the study provides clear evidence of the gut microbiome's capability to serve as a biomarker for PC detection, emphasizing the steps required for further differentiation among its diverse phenotypes.

Stable, fluorescent markers for tracking synthetic communities and assembly dynamics

BackgroundAfter two decades of extensive microbiome research, the current forefront of scientific exploration involves moving beyond description and classification to uncovering the intricate mechanisms underlying the coalescence of microbial communities. Deciphering microbiome assembly has been technically challenging due to their vast microbial diversity but establishing a synthetic community (SynCom) serves as a key strategy in unravelling this process. Achieving absolute quantification is crucial for establishing causality in assembly dynamics. However, existing approaches are primarily designed to differentiate a specific group of microorganisms within a particular SynCom.ResultsTo address this issue, we have developed the differential fluorescent marking (DFM) strategy, employing three distinguishable fluorescent proteins in single and double combinations. Building on the mini-Tn7 transposon, DFM capitalises on enhanced stability and broad applicability across diverse Proteobacteria species. The various DFM constructions are built using the pTn7-SCOUT plasmid family, enabling modular assembly, and facilitating the interchangeability of expression and antibiotic cassettes in a single reaction. DFM has no detrimental effects on fitness or community assembly dynamics, and through the application of flow cytometry, we successfully differentiated, quantified, and tracked a diverse six-member SynCom under various complex conditions like root rhizosphere showing a different colonisation assembly dynamic between pea and barley roots.ConclusionsDFM represents a powerful resource that eliminates dependence on sequencing and/or culturing, thereby opening new avenues for studying microbiome assembly.CZiF6AaEp7jbjKaYRmvD15Video