Microbiome Datasets Research Articles

Kaempferol Remodels Liver Monocyte Populations and Treats Hepatic Fibrosis in Mice by Modulating Intestinal Flora and Metabolic Reprogramming.

Changes in gut flora are associated with liver fibrosis. The interactions of host with intestinal flora are still unknown, with little research investigating such interactions with comprehensive multi-omics data. The present work analyzed and integrated large-scale multi-omics transcriptomics, microbiome, metabolome, and single-cell RNA-sequencing datasets from Kaempferol-treated and untreated control groups by advanced bioinformatics methods. This study concludes that kaempferol dose-dependently improved serum markers (like AST, ALT, TBil, Alb, and PT) and suppressed fibrosis markers (including HA, PC III, LN, α-SMA, and Collagen I), while kaempferol also increased body weight. Mechanistically, kaempferol improved the metabolic levels of intestinal flora dysbiosis and associated lipids. This was achieved by increasing the abundance of g__Robinsoniella, g__Erysipelotrichaceae_UCG-003, g__Coriobacteriaceae_UCG-002, and 5-Methylcytidine, all-trans-5,6- Epoxyretinoic acid, LPI (18:0), LPI (20:4), etc. to achieve this. Kaemferol exerts anti-inflammatory and immune-enhancing effects by down-regulating the Th17/IL-17 signaling pathway in PDGF-induced LX2 cells. In addition, kaempferol administration remarkably elevated CD4 + T and CD8 + T cellular proportions, thereby activating immune cells for protecting the body and controlling inflammatory conditions. The combined interaction of multiple data may explain how Kaempferol modulates the intestinal flora thereby remodeling the hepatocyte population and alleviating liver fibrosis.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome.

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles andFR profiles, and identified 14636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Gill-associated ammonia oxidizers are widespread in teleost fish.

Recent advances in sequencing methods have greatly expanded the knowledge of teleost-associated microorganisms. While fish-gut microbiomes are comparatively well studied, less attention has gone toward other, external organ-microbiome associations. Gills are particularly interesting to investigate due to their functions in gas exchange, osmoregulation, and nitrogen excretion. We recently discovered a branchial symbiosis between nitrogen-cycling bacteria and teleosts (zebrafish and carp), in which ammonia-oxidizing Nitrosomonas and denitrifying bacteria together convert toxic ammonia excreted by the fish into harmless dinitrogen (N2) gas. This symbiosis can function as a "natural biofilter" in fish gills and can potentially occur in all ammonotelic fish species, but it remains unknown how widespread this symbiosis is. In this study, we analyzed all publicly available gill microbiome data sets and checked for the presence of Nitrosomonas. We discovered that more than half of the described fish gill microbiomes contain 16S rRNA gene sequences of ammonia-oxidizing bacteria (AOB). The presence of gill-specific AOB was shown in both wild and aquacultured fish, as well as in marine and freshwater fish species. Based on these findings, we propose that ammonia oxidizers are widespread in teleost fish gills. These gill-associated AOB can significantly affect fish nitrogen excretion, and the widespread nature of this association suggests that the gill-associated AOB can have similar impacts on more fish species. Future research should address the contribution of these microorganisms to fish nitrogen metabolism and the fundamental characteristics of this novel symbiosis.IMPORTANCERecent advances in sequencing have increased our knowledge of teleost-associated microbiota, but the gill microbiome has received comparatively little attention. We recently discovered a consortium of nitrogen-cycling bacteria in the gills of common carp and zebrafish, which are able to convert (toxic) ammonia into harmless dinitrogen gas. These microorganisms thus function as a natural nitrogen biofilter. We analyzed all available gill microbiome data sets to determine how widespread gill-associated ammonia-oxidizing bacteria (AOB) are. More than half of the data sets contained AOB, representing both aquacultured and wild fish from freshwater and marine habitats. In total, 182 amplicon sequencing variants were obtained, of which 115 were found specifically in the gills and not the environmental microbiomes. As gill-associated AOB are apparently widespread in teleost fish, it is important to study their impact on host nitrogen excretion and the potential to reduce ammonia accumulation in (recirculating) aquaculture of relevant fish species.

Unveiling the causal effects of gut microbiome on trimethylamine N-oxide: evidence from Mendelian randomization.

The relationship between gut microbiome and trimethylamine oxide (TMAO) has not been fully elucidated. We aimed to assess the causal effects of different gut microbes on TMAO using Mendelian randomization (MR). Gut microbiome and TMAO datasets were acquired from genome-wide association studies and screened for single nucleotide polymorphisms according to the basic assumptions of MR. Inverse variance weighted was used as the main method in MR analysis to assess the causal relationship between the gut microbiome and TMAO. Finally, the MR-Egger intercept, Cochran's Q test, and leave-one-out sensitivity analysis were used to assess the horizontal pleiotropy, heterogeneity, and robustness of the results, respectively. MR analysis revealed that the species Bacteroides finegoldii (odds ratio [OR] 1.064, 95% confidence interval [CI] 1.003 to 1.128, p = 0.039), family Sutterellaceae (OR 1.188, 95% CI 1.003 to 1.407, p = 0.047), and phylum Pseudomonadota (OR 1.205, 95% CI 1.036 to 1.401, p = 0.016), as well as the species Bacteroides uniformis (OR 1.263, 95% CI 1.039 to 1.535, p = 0.019), were positively associated with increased genetic susceptibility to TMAO. In contrast, the species Bacteroides thetaiotaomicron (OR 0.813, 95% CI 0.696 to 0.950, p = 0.009) and Bilophila wadsworthia (OR 0.828, 95% CI 0.690 to 0.995, p = 0.044) were associated with reduced genetic susceptibility to TMAO. Additionally, the MR-Egger intercept indicated no horizontal pleiotropy (p ≥ 0.05), and Cochran's Q test and sensitivity analysis demonstrated that the results were not heterogeneous (p ≥ 0.05) and were robust. Our findings revealed the role of the phylum Pseudomonadota, family Sutterellaceae, species Bacteroides finegoldii, and Bacteroides uniformis in increasing TMAO, as well as the species Bacteroides thetaiotaomicron and Bilophila wadsworthia in decreasing TMAO. This study provides new insights into the relationship between the gut microbiome and TMAO levels.

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data.

Over the past decade, the prevalence of inflammatory bowel disease (IBD) has significantly increased, making early detection crucial for improving patient survival rates. Medical research suggests that changes in the human gut microbiome are closely linked to IBD onset, playing a critical role in its prediction. However, the current gut microbiome data often exhibit missing values and high dimensionality, posing challenges to the accuracy of predictive algorithms. To address these issues, we proposed the comprehensive data optimization and risk prediction framework (CDORPF), an ensemble learning framework designed to predict IBD risk based on the human gut microbiome, aiding early diagnosis. The framework comprised two main components: data optimization and risk prediction. The data optimization module first employed triple optimization imputation (TOI) to impute missing data while preserving the biological characteristics of the microbiome. It then utilized importance-weighted variational autoencoder (IWVAE) to reduce redundant information from the high-dimensional microbiome data. This process resulted in a complete, low-dimensional representation of the data, laying the foundation for improved algorithm efficiency and accuracy. In the risk prediction module, the optimized data was classified using a random forest (RF) model, and hyperparameters were globally optimized using improved aquila optimizer (IAO), which incorporated multiple strategies. Experimental results on IBD-related gut microbiome datasets showed that the proposed framework achieved classification accuracy, recall, and F1 scores exceeding 0.9, outperforming comparison models and serving as a valuable tool for predicting IBD onset risk.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Stable biomarker discovery in multi-omics data via canonical correlation analysis.

Multi-omics analysis offers a promising avenue to a better understanding of complex biological phenomena. In particular, untangling the pathophysiology of multifactorial health conditions such as the inflammatory bowel disease (IBD) could benefit from simultaneous consideration of several omics levels. However, taking full advantage of multi-omics data requires the adoption of suitable new tools. Multi-view learning, a machine learning technique that natively joins together heterogeneous data, is a natural source for such methods. Here we present a new approach to variable selection in unsupervised multi-view learning by applying stability selection to canonical correlation analysis (CCA). We apply our method, StabilityCCA, to simulated and real multi-omics data, and demonstrate its ability to find relevant variables and improve the stability of variable selection. In a case study on an IBD microbiome data set, we link together metagenomics and metabolomics, revealing a connection between their joint structure and the disease, and identifying potential biomarkers. Our results showcase the usefulness of multi-view learning in multi-omics analysis and demonstrate StabilityCCA as a powerful tool for biomarker discovery.

Gender-specific microbial signatures in saliva: Unveiling the association between the oral microbiome and the pathogenesis of glioma

The intricate interplay between the human oral microbiome and systemic health is increasingly being recognized, particularly in the context of central nervous system pathologies such as glioblastoma. In this study, we aimed to elucidate gender-specific differences in the salivary microbiome of glioma patients by utilizing 16S rRNA sequencing data from publicly available salivary microbiome datasets. We conducted comprehensive bioinformatics analysis, encompassing quality control, noise reduction, species classification, and microbial community composition analysis at various taxonomic levels. Machine learning algorithms were employed to identify microbial signatures associated with glioma. When compared to healthy controls, our analysis revealed distinct differences in the salivary microbiota of glioma patients. Notably, the genera Leptotrichia and Atopobium exhibited significant variations in abundance between genders. Leptotrichia was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, Atopobium was more abundant in male glioma patients. These findings suggest that hormonal influences might play a role in shaping the salivary microbiome and its association with glioma. We utilized a combination of LASSO-logistic regression and random forest models for feature selection, and identified key microbial features that differentiated glioma patients from healthy controls. We developed a diagnostic model with high predictive accuracy and area under the curve and principal component analysis metrics confirmed its robustness. The analysis of microbial markers, including Atopobium and Leptotrichia, highlighted the potential of the salivary microbiota as a non-invasive biomarker for the diagnosis and prognosis of glioma. Our findings highlight significant gender-specific disparities in the salivary microbiome of patients with glioma, offering new insights into the pathogenesis of glioma and paving the way for innovative diagnostic and therapeutic strategies. The use of saliva as a diagnostic fluid, given its ease of collection and non-invasive nature, holds immense promise for monitoring systemic health and the trajectory of disease. Future research should focus on investigating the underlying mechanisms by which the salivary microbiome influences the development of glioma and identifying potential microbiome-targeted therapies to enhance the management of glioma.

Rank-based correlation matrix estimation for high dimensional microbiome data

In recent years, the progress made in high-throughout sequencing techniques supports the analysis of microbial communities greatly. The fact that real-world microbiome data lie in a high-dimensional simplex makes conventional statistical analysis unreliable owing to the sum-to-one constraint. Motivated by the urgent requirements of directly inferring the dependence pattern and dependence intensity simultaneously among microbial taxa, we address the challenge of correlation matrix estimation for this kind of data. Assuming that the unobserved log-basis random vectors follow the elliptical distribution, a robust method built on the Kendall's tau statistic is developed to estimate the basis generalized correlation matrix. Regardless of the existence of the covariance, the basis generalized correlation matrix is always a reliable measure of dependence structure. Theoretically, we establish the convergence rate of the proposed estimator under the spectral norm. Support recovery is discussed as well. Empirically, simulation studies demonstrate the outstanding numerical performance of our method. We also apply our method to analyse a microbiome dataset from human gut. We provide all the codes at https://github.com/lwfwhunanhero/CRCE.

Host diet drives gut microbiome convergence between coral reef fishes and mammals.

Animal gut microbiomes are critical to host physiology and fitness. The gut microbiomes of fishes-the most abundant and diverse vertebrate clade-have received little attention relative to other clades. Coral reef fishes, in particular, make up a wide range of evolutionary histories and feeding ecologies that are likely associated with gut microbiome diversity. The repeated evolution of herbivory in fishes and mammals also allows us to examine microbiome similarity in relationship to diet across the entire vertebrate tree of life. Here, we generate a large coral reef fish gut microbiome dataset (n = 499 samples, 19 species) and combine it with a diverse aggregation of public microbiome data (n = 447) to show that host diet drives significant convergence between coral reef fish and mammalian gut microbiomes. We demonstrate that this similarity is largely driven by carnivory and herbivory and that herbivorous and carnivorous hosts exhibit distinct microbial compositions across fish and mammals. We also show that fish and mammal gut microbiomes share prominent microbial taxa, including Ruminoccocus spp. and Akkermansia spp., and predicted metabolic pathways. Despite the major evolutionary and ecological differences between fishes and mammals, our results reveal that their gut microbiomes undergo similar dietary selective pressures. Thus, diet, in addition to phylosymbiosis must be considered even when comparing the gut microbiomes of distantly related hosts.

Interpolation of microbiome composition in longitudinal data sets.

Since missing samples are common in longitudinal microbiome dataset due to inconsistent collection practices, it is important to evaluate and benchmark different interpolation methods for predicting microbiome composition in such samples and facilitate downstream analysis. Our study rigorously evaluated several such methods and identified the K-nearest neighbors approach as particularly effective for this task. The study also notes significant variability in interpolation accuracy among individuals, influenced by factors such as age, sample size, and sampling frequency. Furthermore, we developed a predictive model for estimating interpolation accuracy at a specific time point, enhancing the reliability of such analyses in future studies. Combined, our study, thus, provides critical insights and tools that enhance the accuracy and reliability of data interpolation methods in the growing field of longitudinal microbiome research.

An optimal normalization method for high sparse compositional microbiome data.

In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Early Detection of Food Safety and Spoilage Incidents Based on Live Microbiome Profiling and PMA-qPCR Monitoring of Indicators.

The early detection of spoilage microorganisms and food pathogens is of paramount importance in food production systems. We propose a novel strategy for the early detection of food production defects, harnessing the product microbiome. We hypothesize that by establishing microbiome datasets of proper and defective batches, indicator bacteria signaling production errors can be identified and targeted for rapid quantification as part of routine practice. Using the production process of pastrami as a model, we characterized its live microbiome profiles throughout the production stages and in the final product, using propidium monoazide treatment followed by 16S rDNA sequencing. Pastrami demonstrated product-specific and consistent microbiome profiles predominated by Serratia and Vibrionimonas, with distinct microbial signatures across the production stages. Based on the established microbiome dataset, we were able to detect shifts in the microbiome profile of a defective batch produced under lactate deficiency. The most substantial changes were observed as increased relative abundances of Vibrio and Lactobacillus, which were subsequently defined as potential lactate-deficiency indicators. PMA-qPCR efficiently detected increased levels of these species, thus proving useful in rapidly pinpointing the production defect. This approach offers the possibility of the in-house detection of defective production events with same-day results, promoting safer food production systems.

Mixtures of logistic normal multinomial regression models for microbiome data

In the realm of bioinformatics, we frequently encounter discrete data, particularly microbiome taxa count data obtained through 16S rRNA sequencing. These microbiome datasets are commonly characterized by their high dimensionality and the ability to provide insights solely into relative abundance, necessitating their classification as compositional data. Analyzing such data presents challenges due to their confinement within a simplex. Additionally, microbiome taxa counts are subject to influence by various biological and environmental factors like age, gender, and diet. Thus, we have developed a novel approach involving regression-based mixtures of logistic normal multinomial models for clustering microbiome data. These models effectively categorize samples into more homogeneous subpopulations, enabling the exploration of relationships between bacterial abundance and biological or environmental covariates within each identified group. To enhance the accuracy and efficiency of parameter estimation, we employ a robust framework based on variational Gaussian approximations (VGA). Our proposed method's effectiveness is demonstrated through its application to simulated and real datasets.

MIDASim: a fast and simple simulator for realistic microbiome data

BackgroundAdvances in sequencing technology has led to the discovery of associations between the human microbiota and many diseases, conditions, and traits. With the increasing availability of microbiome data, many statistical methods have been developed for studying these associations. The growing number of newly developed methods highlights the need for simple, rapid, and reliable methods to simulate realistic microbiome data, which is essential for validating and evaluating the performance of these methods. However, generating realistic microbiome data is challenging due to the complex nature of microbiome data, which feature correlation between taxa, sparsity, overdispersion, and compositionality. Current methods for simulating microbiome data are deficient in their ability to capture these important features of microbiome data, or can require exorbitant computational time.MethodsWe develop MIDASim (MIcrobiome DAta Simulator), a fast and simple approach for simulating realistic microbiome data that reproduces the distributional and correlation structure of a template microbiome dataset. MIDASim is a two-step approach. The first step generates correlated binary indicators that represent the presence-absence status of all taxa, and the second step generates relative abundances and counts for the taxa that are considered to be present in step 1, utilizing a Gaussian copula to account for the taxon-taxon correlations. In the second step, MIDASim can operate in both a nonparametric and parametric mode. In the nonparametric mode, the Gaussian copula uses the empirical distribution of relative abundances for the marginal distributions. In the parametric mode, a generalized gamma distribution is used in place of the empirical distribution.ResultsWe demonstrate improved performance of MIDASim relative to other existing methods using gut and vaginal data. MIDASim showed superior performance by PERMANOVA and in terms of alpha diversity and beta dispersion in either parametric or nonparametric mode. We also show how MIDASim in parametric mode can be used to assess the performance of methods for finding differentially abundant taxa in a compositional model.ConclusionsMIDASim is easy to implement, flexible and suitable for most microbiome data simulation situations. MIDASim has three major advantages. First, MIDASim performs better in reproducing the distributional features of real data compared to other methods, at both the presence-absence level and the relative-abundance level. MIDASim-simulated data are more similar to the template data than competing methods, as quantified using a variety of measures. Second, MIDASim makes few distributional assumptions for the relative abundances, and thus can easily accommodate complex distributional features in real data. Third, MIDASim is computationally efficient and can be used to simulate large microbiome datasets.55nwZAUMVt5VGoVjkFQM1tVideo

Causal associations among gut microbiota, 1400 plasma metabolites, and asthma: a two-sample Mendelian randomization study.

Emerging evidence indicates a correlation between imbalances in intestinal microbiota and changes in plasma metabolites in the progression of asthma. However, the causal link between these factors remains unclear. A two-sample Mendelian randomization (MR) study was employed to evaluate the potential causal connection between gut microbiota, plasma metabolites, and asthma susceptibility. Gut microbiota data from expansive genome-wide genotype studies and 16S fecal microbiome datasets were examined by the MiBioGen Alliance. Asthma data were procured from the FinnGen biobank analysis, while comprehensive Genome-Wide Association Studies (GWAS) summary statistics for plasma metabolites were derived from the NHGRI-EBI GWAS Catalog. Fluctuations in intestinal flora and plasma metabolites in asthma patients were evaluated using the weighted mode method. Additionally, pleiotropic and heterogeneity analyses were performed to ascertain the reliability of the findings. Upon examining the gut microbiota through MR with the IVW method, alongside tests for heterogeneity and pleiotropy, findings reveal a negative association between the abundance of the Christensenellaceae R.7 group and asthma risk. In contrast, the Bifidobacterium and Prevotella 7 genera exhibit a positive association with asthma risk, indicating they may be potential risk factors (p < 0.05). Furthermore, MR analysis of 1,400 metabolites employing Weighted median, IVW, and Weighted mode methods resulted in p-values below 0.05. Subsequent tests for pleiotropy and heterogeneity showed that the levels of 3,5-dichloro-2,6-dihydroxybenzoic acid have a negative correlation with asthma, whereas the phenylalanine to phosphate ratio has a positive correlation, suggesting their potential as risk factors for asthma (p < 0.05). The current Mendelian randomization study provides evidence supporting a potential causal link between specific gut microbiota taxa, plasma metabolites, and asthma. These findings offer novel perspectives for future research and the development of treatment and prevention strategies for asthma.

DeepPIG: deep neural network architecture with pairwise connected layers and stochastic gates using knockoff frameworks for feature selection

Selecting relevant feature subsets is essential for machine learning applications. Among the feature selection techniques, the knockoff filter procedure proposes a unique framework that minimizes false discovery rates (FDR). However, employing a deep neural network architecture for a knockoff filter framework requires higher detection power. Using the knockoff filter framework, we present a Deep neural network with PaIrwise connected layers integrated with stochastic Gates (DeepPIG) for the feature selection model. DeepPIG exhibited better detection power in synthetic data than the baseline and recent models such as Deep feature selection using Paired-Input Nonlinear Knockoffs (DeepPINK), Stochastic Gates (STG), and SHapley Additive exPlanations (SHAP) while not violating the preselected FDR level, especially when the signal of the features were weak. The selected features determined by DeepPIG demonstrated superior classification performance compared with the baseline model in real-world data analyses, including the prediction of certain cancer prognosis and classification tasks using microbiome and single-cell datasets. In conclusion, DeepPIG is a robust feature selection approach even when the signals of features are weak. Source code is available at https://github.com/DMCB-GIST/DeepPIG.

Guideline for designing microbiome studies in neoplastic diseases

Oncobiosis has emerged as a key contributor to the development, and modulator of the treatment efficacy of cancer. Hereby, we review the modalities through which the oncobiome can support the progression of tumors, and the emerging therapeutic opportunities they present. The review highlights the inherent challenges and limitations faced in sampling and accurately characterizing oncobiome. Additionally, the review underscores the critical need for the standardization of microbial analysis techniques and the consistent reporting of microbiome data. We provide a suggested metadata set that should accompany microbiome datasets from oncological settings so that studies remain comparable and decipherable.

Absence of association between maternal adverse events and long-term gut microbiome outcomes in the Australian autism biobank

IntroductionMaternal immune activation (MIA) and prenatal maternal stress (MatS) are well-studied risk factors for psychiatric conditions such as autism and schizophrenia. Animal studies have proposed the gut microbiome as a mechanism underlying this association and have found that risk factor-related gut microbiome alterations persist in the adult offspring. In this cross-sectional study, we assessed whether maternal immune activation and prenatal maternal stress were associated with long-term gut microbiome alterations in children using shotgun metagenomics. MethodsThis cross-sectional study included children diagnosed with autism (N = 92), siblings without a diagnosis (N = 42), and unrelated children (N = 40) without a diagnosis who were recruited into the Australian Autism Biobank and provided a faecal sample. MIA exposure was inferred from self-reported data and included asthma/allergies, complications during pregnancy triggering an immune response, auto-immune conditions, and acute inflammation. Maternal stress included any of up to 9 stressful life events during pregnancy, such as divorce, job loss, and money problems. Data were analysed for a total of 174 children, of whom 63 (36%) were born to mothers with MIA and 84 (48%) were born to mothers who experienced maternal stress during pregnancy (where 33 [19%] experienced both). Gut microbiome data was assessed using shotgun metagenomic sequencing of the children's faecal samples. ResultsIn our cohort, MIA, but not MatS, was associated with ASD. Variance component analysis revealed no associations between any of the gut microbiome datasets and neither MIA nor MatS. After adjusting for age, sex, diet and autism diagnosis, there was no significant difference between groups for bacterial richness, α-diversity or β-diversity. We found no significant differences in species abundance in the main analyses. However, when stratifying the cohort by age, we found that Faecalibacterium prausnitzii E was significantly decreased in MIA children aged 11–17. DiscussionConsistent with previous findings, we found that children who were born to mothers with MIA were more likely to be diagnosed with autism. Unlike within animal studies, we found negligible microbiome differences associated with MIA and maternal stress. Given the current interest in the microbiome-gut-brain axis, researchers should exercise caution in translating microbiome findings from animal models to human contexts and the clinical setting.