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Differential abundance (DA) analysis of metagenomic microbiome data is essential for understanding microbial community dynamics across various environments and hosts. Identifying microorganisms that differ significantly in abundance between conditions (e.g., health vs. disease) is crucial for insights into environmental adaptations, disease development, and host health. However, the statistical interpretation of microbiome data is challenged by inherent sparsity and compositional nature, necessitating tailored DA methods. This benchmarking study aims to simulate synthetic 16S microbiome data using metaSPARSim (Patuzzi I, Baruzzo G, Losasso C, Ricci A, Di Camillo B. MetaSPARSim: a 16S rRNA gene sequencing count data simulator. BMC Bioinformatics. 2019;20:416. https://doi.org/10.1186/s12859-019-2882-6 PMID: 31757204) MIDASim (He M, Zhao N, Satten GA. MIDASim: a fast and simple simulator for realistic microbiome data. Available from: https://doi.org/10.1101/2023.03.23.533996 ), and sparseDOSSA2 (Ma S, Ren B, Mallick H, Moon YS, Schwager E, Maharjan S, et al. A statistical model for describing and simulating microbial community profiles. PLOS Comput Biol. 2021;17(9):e1008913. https://doi.org/10.1371/journal.pcbi.1008913 PMID: 34516542) , leveraging 38 real-world experimental templates (S3 Table) previously utilized in a benchmark study comparing DA tools. These datasets, drawn from diverse environments such as human gut, soil, and marine habitats, serve as the foundation for our simulation efforts. We employ the same 14 DA tests that were previously used with the same experimental data in benchmark studies alongside 8 DA tests that were developed subsequently. Initially, we will generate synthetic data closely mirroring the experimental datasets, incorporating a known truth to cover a broad range of real-world data characteristics. This approach allows us to assess the ability of DA methods to recover known true differential abundances. We will further simulate datasets by altering sparsity, effect size, and sample size, thus creating a comprehensive collection for applying the 22 DA tests. The outcomes, focusing on sensitivities and specificities, will provide insights into the performance of DA tests and their dependencies on sparsity, effect size, and sample size. Additionally, we will calculate data characteristics (S1 and S2 Table) for each simulated dataset and use a multiple regression to identify informative data characteristics influencing test performance. Our prior study, where we used simulated data without incorporating a known truth, demonstrated the feasibility of using synthetic data to validate experimental findings. This current study aims to enhance our understanding by systematically evaluating the impact of known truth incorporation on DA test performance, thereby providing further information for the selection and application of DA methods in microbiome research.

Background Synthetic data's utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results obtained from experimental data. Building on Nearing et al.'s study (1), who assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we are generating synthetic datasets that mimic the experimental data to verify their findings. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines, demonstrating how established reporting frameworks can support robust, transparent, and unbiased study planning. Methods We replicate Nearing et al.'s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring the 38 experimental datasets. Equivalence tests will be conducted on a non-redundant subset of 46 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results. Conclusions Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, try to validate previous findings with the most recent versions of the DA methods and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing to transparency, reproducibility, and unbiased research.

Systemic immunity supports lifelong brain function. Obesity posits a chronic burden on systemic immunity. Independently, obesity was shown as a risk factor for Alzheimer’s disease (AD). Here we show that high-fat obesogenic diet accelerated recognition-memory impairment in an AD mouse model (5xFAD). In obese 5xFAD mice, hippocampal cells displayed only minor diet-related transcriptional changes, whereas the splenic immune landscape exhibited aging-like CD4 + T-cell deregulation. Following plasma metabolite profiling, we identified free N -acetylneuraminic acid (NANA), the predominant sialic acid, as the metabolite linking recognition-memory impairment to increased splenic immune-suppressive cells in mice. Single-nucleus RNA-sequencing revealed mouse visceral adipose macrophages as a potential source of NANA. In vitro, NANA reduced CD4 + T-cell proliferation, tested in both mouse and human. In vivo, NANA administration to standard diet-fed mice recapitulated high-fat diet effects on CD4 + T cells and accelerated recognition-memory impairment in 5xFAD mice. We suggest that obesity accelerates disease manifestation in a mouse model of AD via systemic immune exhaustion. Obesity and aging increase Alzheimer’s disease (AD) risk. Here, using an AD mouse model and high-fat diet, we suggest that immune exhaustion links the two risk factors, and identify a metabolite that can hasten immune dysfunction and memory deficit.

Postoperative complications are a major problem occurring in up to 50% of patients undergoing major abdominal surgery. Occurrence of postoperative complications is associated with a significantly higher morbidity and mortality in affected patients. The most common postoperative complications are caused by an infectious genesis and include anastomotic leakage in case of gastrointestinal anastomosis and surgical site infections. Recent research highlighted the importance of gut microbiota in health and disease. It is plausible that the gut microbiota also plays a pivotal role in the development of postoperative complications. This narrative review critically summarizes results of recent research in this particular field. The review evaluates the role of gut microbiota alteration in postoperative complications, including postoperative ileus, anastomotic leakage, and surgical site infections in visceral surgery. We tried to put a special focus on a potential diagnostic value of pre- and post-operative gut microbiota sampling showing that recent data are inhomogeneous to identify a high-risk microbial profile for development of postoperative complications.

Alterations of the gut microbiome are involved in the pathogenesis of Crohn's disease (CD). The role of fungi in this context is unclear. This study aimed to determine postoperative changes in the bacterial and fungal gut communities of CD patients undergoing intestinal resection, and to evaluate interactions between the bacteriome and mycobiome and their impact on the patients' outcome. We report a subgroup analysis of a prospective cohort study, focusing on 10 CD patients whose fecal samples were collected for bacterial 16S rRNA and fungal ITS2 genes next-generation sequencing the day before surgery and on the 5th or 6th postoperative day. No significant differences in bacterial and fungal diversity were observed between preoperative and postoperative stool samples. By in-depth analysis, significant postoperative abundance changes of bacteria and fungi and 17 interkingdom correlations were detected. Network analysis identified 13 microbial clusters in the perioperative gut communities, revealing symbiotic and competitive interactions. Relevant factors were gender, age, BMI, lifestyle habits (smoking, alcohol consumption) and surgical technique. Postoperative abundance changes and identified clusters were associated with clinical outcomes (length of hospital stay, complications) and levels of inflammatory markers. Our findings highlight the importance of dissecting the interactions of gut bacterial and fungal communities in CD patients and their potential influence on postoperative and disease outcomes.

An essential role of the gut microbiota in health and disease is strongly suggested by recent research. The composition of the gut microbiota is modified by multiple internal and external factors, such as diet. A vegan diet is known to show beneficial health effects, yet the role of the gut microbiota is unclear. Within a 4-week, monocentric, randomized, controlled trial with a parallel group design (vegan (VD) vs. meat-rich (MD)) with 53 healthy, omnivore, normal-weight participants (62% female, mean 31 years of age), fecal samples were collected at the beginning and at the end of the trial and were analyzed using 16S rRNA gene amplicon sequencing (Clinical Trial register: DRKS00011963). Alpha diversity as well as beta diversity did not differ significantly between MD and VD. Plotting of baseline and end samples emphasized a highly intra-individual microbial composition. Overall, the gut microbiota was not remarkably altered between VD and MD after the trial. Coprococcus was found to be increased in VD while being decreased in MD. Roseburia and Faecalibacterium were increased in MD while being decreased in VD. Importantly, changes in genera Coprococcus, Roseburia and Faecalibacterium should be subjected to intense investigation as markers for physical and mental health.

Cancers of the biliary tract are more common in Asia than in Europe, but are highly lethal due to delayed diagnosis and aggressive tumor biology. Since the biliary tract is in direct contact with the gut via the enterohepatic circulation, this suggests a potential role of gut microbiota, but to date, the role of gut microbiota in biliary tract cancers has not been elucidated. This scoping review compiles recent data on the associations between the gut microbiota and diagnosis, progression and prognosis of biliary tract cancer patients. Systematic review of the literature yielded 154 results, of which 12 studies and one systematic review were eligible for evaluation. The analyses of microbiota diversity indices were inconsistent across the included studies. In-depth analyses revealed differences between gut microbiota of biliary tract cancer patients and healthy controls, but without a clear tendency towards particular species in the studies. Additionally, most of the studies showed methodological flaws, for example non-controlling of factors that affect gut microbiota. At the current stage, there is a lack of evidence to support a general utility of gut microbiota diagnostics in biliary tract cancers. Therefore, no recommendation can be made at this time to include gut microbiota analyses in the management of biliary tract cancer patients.

Background Synthetic data’s utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results from experimental data. Building on Nearing et al.‘s study (1), which assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we generated synthetic datasets to verify these findings. We rigorously assessed the similarity between synthetic and experimental data and validated the conclusions on the performance of these tests drawn by Nearing et al. (1). This study adheres to the study protocol: Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data (2). Methods We replicated Nearing et al.’s (1) methodology, incorporating simulated data using two distinct tools (metaSPARSim (3) and sparseDOSSA2 (4)), mirroring the 38 experimental datasets. Equivalence tests were conducted on a set of 30 data characteristics (DC) comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests were applied to synthetic datasets, evaluating the consistency of significant feature identification and the proportion of significant features per tool. Correlation analysis, multiple regression and decision trees were used to explore how differences between synthetic and experimental DCs may affect the results. Conclusions Adhering to a formal study protocol in computational benchmarking studies is crucial for ensuring transparency and minimizing bias, though it comes with challenges, including significant effort required for planning, execution, and documentation. In this study, metaSPARSim (3) and sparseDOSSA2 (4) successfully generated synthetic data mirroring the experimental templates, validating trends in differential abundance tests. Of 27 hypotheses tested, 6 were fully validated, with similar trends for 37%. While hypothesis testing remains challenging, especially when translating qualitative observations from text into testable hypotheses, synthetic data for validation and benchmarking shows great promise for future research.

Differential abundance (DA) analysis of metagenomic microbiome data is essential for understanding microbial community dynamics across various environments and hosts. Identifying microorganisms that differ significantly in abundance between conditions (e.g., health vs. disease) is crucial for insights into environmental adaptations, disease development, and host health. However, the statistical interpretation of microbiome data is challenged by inherent sparsity and compositional nature, necessitating tailored DA methods. This benchmarking study aims to simulate synthetic 16S microbiome data using metaSPARSim (Patuzzi I, Baruzzo G, Losasso C, Ricci A, Di Camillo B. MetaSPARSim: a 16S rRNA gene sequencing count data simulator. BMC Bioinformatics. 2019;20:416. https://doi.org/10.1186/s12859-019-2882-6 PMID: 31757204) MIDASim (He M, Zhao N, Satten GA. MIDASim: a fast and simple simulator for realistic microbiome data. Available from: https://doi.org/10.1101/2023.03.23.533996), and sparseDOSSA2 (Ma S, Ren B, Mallick H, Moon YS, Schwager E, Maharjan S, et al. A statistical model for describing and simulating microbial community profiles. PLOS Comput Biol. 2021;17(9):e1008913. https://doi.org/10.1371/journal.pcbi.1008913 PMID: 34516542) , leveraging 38 real-world experimental templates (S3 Table) previously utilized in a benchmark study comparing DA tools. These datasets, drawn from diverse environments such as human gut, soil, and marine habitats, serve as the foundation for our simulation efforts. We employ the same 14 DA tests that were previously used with the same experimental data in benchmark studies alongside 8 DA tests that were developed subsequently. Initially, we will generate synthetic data closely mirroring the experimental datasets, incorporating a known truth to cover a broad range of real-world data characteristics. This approach allows us to assess the ability of DA methods to recover known true differential abundances. We will further simulate datasets by altering sparsity, effect size, and sample size, thus creating a comprehensive collection for applying the 22 DA tests. The outcomes, focusing on sensitivities and specificities, will provide insights into the performance of DA tests and their dependencies on sparsity, effect size, and sample size. Additionally, we will calculate data characteristics (S1 and S2 Table) for each simulated dataset and use a multiple regression to identify informative data characteristics influencing test performance. Our prior study, where we used simulated data without incorporating a known truth, demonstrated the feasibility of using synthetic data to validate experimental findings. This current study aims to enhance our understanding by systematically evaluating the impact of known truth incorporation on DA test performance, thereby providing further information for the selection and application of DA methods in microbiome research.

Background The utility of synthetic data in benchmark studies depends on its ability to closely mimic real-world conditions and to reproduce results obtained from experimental data. Here, we evaluate the performance of differential abundance tests for 16S metagenomic data. Building on the benchmark study by Nearing et al. (1), who assessed 14 differential abundance tests using 38 experimental datasets in a case-control design, we validate their findings by generating synthetic datasets that mimics the experimental data. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines and is, to our knowledge, the first of its kind in computational benchmark studies. Methods We replicate Nearing et al.'s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring each of the 38 experimental datasets. Equivalence tests will be conducted on 43 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to both synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results. Conclusions Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, validate previous findings and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing significantly to transparency, reproducibility, and unbiased research.