A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links

Gut Microbiota Perturbation in IgA Deficiency Is Influenced by IgA-Autoantibody Status

BACKGROUND & AIMS

IgA exerts its primary function at mucosal surfaces, where it binds microbial antigens to regulate bacterial growth and epithelial attachment. One third of individuals with IgA deficiency (IgAD) suffers from recurrent mucosal infections, possibly related to an altered microbiota. We aimed to delineate the impact of IgAD and the IgA-autoantibody status on the composition and functional capacity of the gut microbiota.

METHODS

We performed a paired, lifestyle-balanced analysis of the effect of IgA on the gut microbiota composition and functionality based on fecal samples from individuals with IgAD and IgA-sufficient household members (n = 100), involving quantitative shotgun metagenomics, species-centric functional annotation of gut bacteria, and strain-level analyses. We supplemented the data set with 32 individuals with IgAD and examined the influence of IgA-autoantibody status on the composition and functionality of the gut microbiota.

RESULTS

The gut microbiota of individuals with IgAD exhibited decreased richness and diversity and was enriched for bacterial species encoding pathogen-related functions including multidrug and antimicrobial peptide resistance, virulence factors, and type III and VI secretion systems. These functional changes were largely attributed to Escherichia coli but were independent of E coli strain variations and most prominent in individuals with IgAD with IgA-specific autoreactive antibodies.

CONCLUSIONS

The microbiota of individuals with IgAD is enriched for species holding increased proinflammatory potential, thereby potentially decreasing the resistance to gut barrier-perturbing events. This phenotype is especially pronounced in individuals with IgAD with IgA-specific autoreactive antibodies, thus warranting a screening for IgA-specific autoreactive antibodies in IgAD to identify patients with IgAD with increased risk for gastrointestinal implications.

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

The microbiome has shown to functionally interact with its host or environment at a metabolic level, however the exact nature of these interactions is not well understood. In addition, metabolic dysregulation caused by the microbiome is believed to contribute to the development of diseases such as inflammatory bowel disease, diabetes mellitus, and obesity. In this manuscript, we introduce a computational framework to integrate microbiome and metabolome data to uncover microbe-metabolite interactions in a data-driven manner. Our model uses neural networks to predict metabolite abundances from microbe abundances. The trained models are then used to derive microbe-metabolite feature scores, which are used for clustering microbes and metabolites into functional modules. These module-based interactions are useful in generating biological insights and facilitating hypothesis generation for the investigation of their roles in various metabolic diseases. The software of our model is made freely available to interested researchers.

A practical guide to amplicon and metagenomic analysis of microbiome data

Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Age-related compositional changes and correlations of gut microbiome, serum metabolome, and immune factor in rats

Aging is a complex physiological process associated with degenerative disorder of metabolism and immune function, which contributes to the occurrence of senile diseases. The gut microbiota affects systemic inflammation in aging processes probably through metabolism, but their relationship is still unclear. In this study, 16S-rRNA-sequencing technology, gas chromatography-time-of-flight mass spectrometry (GC-TOFMS)-based metabolic profiling, and immune factor analysis combined with advanced differential and association analysis were employed to investigate the correlation between the microbiome, metabolome, and immune factors in male Wistar rats across lifespan. Our findings showed significant changes in the ileum microbiome and serum metabolome compositions across aging process. A two-level strategy was applied to demonstrate that key metabolites associated with age such as 4-hydroxyproline, proline, and lysine were clustered together and positively correlated with beneficial microbes including Bifidobacterium, Lactobacillus, and Akkermansia. Function analysis explored association between serum metabolite class and specific gut bacteria's metabolism pathways. Further correlation analysis on all the alteration patterns provided an interaction network of main immune factors such as IL-10, IgA, IgM, and IgG with key gut bacteria and serum metabolites. This study offers new insights into the relationship between immune factors, serum metabolome, and the gut microbiome.

Crosstalk between Gut and Brain in Alzheimer's Disease: The Role of Gut Microbiota Modulation Strategies

The gut microbiota (GM) represents a diverse and dynamic population of microorganisms and about 100 trillion symbiotic microbial cells that dwell in the gastrointestinal tract. Studies suggest that the GM can influence the health of the host, and several factors can modify the GM composition, such as diet, drug intake, lifestyle, and geographical locations. Gut dysbiosis can affect brain immune homeostasis through the microbiota-gut-brain axis and can play a key role in the pathogenesis of neurodegenerative diseases, including dementia and Alzheimer's disease (AD). The relationship between gut dysbiosis and AD is still elusive, but emerging evidence suggests that it can enhance the secretion of lipopolysaccharides and amyloids that may disturb intestinal permeability and the blood-brain barrier. In addition, it can promote the hallmarks of AD, such as oxidative stress, neuroinflammation, amyloid-beta formation, insulin resistance, and ultimately the causation of neural death. Poor dietary habits and aging, along with inflammatory responses due to dysbiosis, may contribute to the pathogenesis of AD. Thus, GM modulation through diet, probiotics, or fecal microbiota transplantation could represent potential therapeutics in AD. In this review, we discuss the role of GM dysbiosis in AD and potential therapeutic strategies to modulate GM in AD.

Multi-omics integration in biomedical research - A metabolomics-centric review

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Sex- and age-related trajectories of the adult human gut microbiota shared across populations of different ethnicities

Lifelong sex- and age-related trajectories of the human gut microbiota remain largely unexplored. Using metagenomics, we derived the gut microbial composition of 2,338 adults (26-76 years) from a Han Chinese population-based cohort where metabolic health, hormone levels and aspects of their lifestyles were also recorded. In this cohort, and in three independent cohorts distributed across China, Israel and the Netherlands, we observed sex differences in the gut microbial composition and a shared age-related decrease in sex-dependent differences in gut microbiota. Compared to men, the gut microbiota of premenopausal women exhibited higher microbial diversity and higher abundances of multiple species known to have beneficial effects on host metabolism. We also found consistent sex-independent, age-related gut microbial characteristics across all populations, with the presence of members of the oral microbiota being the strongest indicator of older chronological age. Our findings highlight the existence of sex- and age-related trajectories in the human gut microbiota that are shared between populations of different ethnicities and emphasize the pivotal links between sex hormones, gut microbiota and host metabolism.

Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases

Background: As an important autoimmune disease, type 1 diabetes (T1D) is often diagnosed in children, but due to the complexity of the etiology of diabetes and many other factors, the disease pathogenesis of diabetes is still unclear. The intestinal microbiota has been proved to have close relationships with T1D in recent years, which is one of the most important molecular bases of pathogenesis and prognosis factors for T1D. Using the multi-omics and multicenter sample analysis method, a number of intestinal microbiota in T1D have been discovered and explained, which has provided comprehensive and rich information. However, how to find more useful information and get an intuitive understanding that people need conveniently in the huge data sea has become the focus of attention. Therefore, quantitative analysis and visualization of the interaction between intestinal microbiota and T1D in children are urgently needed. Methods: We retrieved the detailed original data from the National Center for Biotechnology Information, GMREPO, and gutMEGA databases and other authoritative multiple projects with related research; the ranking of intestinal microbiota abundance from healthy people, overall T1D patients, and T1D in children (0-18 years old) were detailed analyzed, classified, and visualized. Results: A total of 515 bacterial species and 161 related genera were fully analyzed. Also, Prevotella copri was led by 21.25% average abundance, followed by Clostridium tertium of 10.39% in all-cross T1D patients. For children with T1D, Bacteroides vulgatus has high abundance in all age periods, whereas the abundance of each intestinal microbiota was more uniform in female samples, with the ranking from high to low as Bacteroides dorei 9.56%, P. copri 9.53%, Streptococcus pasteurianus 8.15%, and C. tertium 7.53%, whereas in male samples, P. copri was accounted for the largest by 22.72%. The interaction between intestinal microbiota and comparison between healthy people and children with T1D was also detailed analyzed. Conclusions: This study provides a new method and comprehensive perspectives for the evaluation of the interaction between intestinal microbiota and T1D in children. A set of useful information of intestinal microbiota with its internal interaction and connections has been presented, which could be a compact, immediate, and practical scientific reference for further molecular biological and clinical translational research of T1D in children.

Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents

OBJECTIVE

Patients with renal failure suffer from symptoms caused by uraemic toxins, possibly of gut microbial origin, as deduced from studies in animals. The aim of the study is to characterise relationships between the intestinal microbiome composition, uraemic toxins and renal failure symptoms in human end-stage renal disease (ESRD).

DESIGN

Characterisation of gut microbiome, serum and faecal metabolome and human phenotypes in a cohort of 223 patients with ESRD and 69 healthy controls. Multidimensional data integration to reveal links between these datasets and the use of chronic kidney disease (CKD) rodent models to test the effects of intestinal microbiome on toxin accumulation and disease severity.

RESULTS

A group of microbial species enriched in ESRD correlates tightly to patient clinical variables and encode functions involved in toxin and secondary bile acids synthesis; the relative abundance of the microbial functions correlates with the serum or faecal concentrations of these metabolites. Microbiota from patients transplanted to renal injured germ-free mice or antibiotic-treated rats induce higher production of serum uraemic toxins and aggravated renal fibrosis and oxidative stress more than microbiota from controls. Two of the species, Eggerthella lenta and Fusobacterium nucleatum, increase uraemic toxins production and promote renal disease development in a CKD rat model. A probiotic Bifidobacterium animalis decreases abundance of these species, reduces levels of toxins and the severity of the disease in rats.

CONCLUSION

Aberrant gut microbiota in patients with ESRD sculpts a detrimental metabolome aggravating clinical outcomes, suggesting that the gut microbiota will be a promising target for diminishing uraemic toxicity in those patients.

TRIAL REGISTRATION NUMBER

This study was registered at ClinicalTrials.gov (NCT03010696).

M2IA: a web server for microbiome and metabolome integrative analysis

MOTIVATION

Microbiome-metabolome association studies have experienced exponential growth for an in-depth understanding of the impact of microbiota on human health over the last decade. However, analyzing the resulting multi-omics data and their correlations remains a significant challenge due to the lack of a comprehensive computational tool that can facilitate data integration and interpretation. In this study, an automated microbiome and metabolome integrative analysis pipeline (M2IA) has been developed to meet the urgent needs for tools that can effectively integrate microbiome and metabolome data to derive biological insights.

RESULTS

M2IA streamlines the integrative data analysis between metabolome and microbiome, from data preprocessing, univariate and multivariate statistical analyses, advanced functional analysis for biological interpretation, to a summary report. The functionality of M2IA was demonstrated using TwinsUK cohort datasets consisting of 1116 fecal metabolites and 16s rRNA microbiome from 786 individuals. Moreover, two important metabolic pathways, i.e. benzoate degradation and phosphotransferase system, were identified to be closely associated with obesity.

AVAILABILITY AND IMPLEMENTATION

M2IA is public available at http://m2ia.met-bioinformatics.cn.

CONTACT

yanni617@zju.edu.cn or fjf68@zju.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antibiotics create a shift from mutualism to competition in human gut communities with a longer-lasting impact on fungi than bacteria

BACKGROUND

Antibiotic treatment has a well-established detrimental effect on the gut bacterial composition, but effects on the fungal community are less clear. Bacteria in the lumen of the gastrointestinal tract may limit fungal colonization and invasion. Antibiotic drugs targeting bacteria are therefore seen as an important risk factor for fungal infections and induced allergies. However, antibiotic effects on gut bacterial-fungal interactions, including disruption and resilience of fungal community compositions, were not investigated in humans. We analysed stool samples collected from 14 healthy human participants over 3 months following a 6-day antibiotic administration. We integrated data from shotgun metagenomics, metatranscriptomics, metabolomics, and fungal ITS2 sequencing.

RESULTS

While the bacterial community recovered mostly over 3 months post treatment, the fungal community was shifted from mutualism at baseline to competition. Half of the bacterial-fungal interactions present before drug intervention had disappeared 3 months later. During treatment, fungal abundances were associated with the expression of bacterial genes with functions for cell growth and repair. By extending the metagenomic species approach, we revealed bacterial strains inhibiting the opportunistic fungal pathogen Candida albicans. We demonstrated in vitro how C. albicans pathogenicity and host cell damage might be controlled naturally in the human gut by bacterial metabolites such as propionate or 5-dodecenoate.

CONCLUSIONS

We demonstrated that antibacterial drugs have long-term influence on the human gut mycobiome. While bacterial communities recovered mostly 30-days post antibacterial treatment, the fungal community was shifted from mutualism towards competition. Video abstract.

Gut microbiota in human metabolic health and disease

Observational findings achieved during the past two decades suggest that the intestinal microbiota may contribute to the metabolic health of the human host and, when aberrant, to the pathogenesis of various common metabolic disorders including obesity, type 2 diabetes, non-alcoholic liver disease, cardio-metabolic diseases and malnutrition. However, to gain a mechanistic understanding of how the gut microbiota affects host metabolism, research is moving from descriptive microbiota census analyses to cause-and-effect studies. Joint analyses of high-throughput human multi-omics data, including metagenomics and metabolomics data, together with measures of host physiology and mechanistic experiments in humans, animals and cells hold potential as initial steps in the identification of potential molecular mechanisms behind reported associations. In this Review, we discuss the current knowledge on how gut microbiota and derived microbial compounds may link to metabolism of the healthy host or to the pathogenesis of common metabolic diseases. We highlight examples of microbiota-targeted interventions aiming to optimize metabolic health, and we provide perspectives for future basic and translational investigations within the nascent and promising research field.

Persistent Alterations in Plasma Lipid Profiles Before Introduction of Gluten in the Diet Associated With Progression to Celiac Disease

Celiac disease (CD) is a chronic enteropathy characterized by an autoimmune reaction in the small intestine of genetically susceptible individuals. The underlying causes of autoimmune reaction and its effect on host metabolism remain largely unknown. Herein, we apply lipidomics to elucidate the early events preceding clinical CD in a cohort of Finnish children, followed up in the Type 1 Diabetes Prediction and Prevention study.

Metabolome-Microbiome Crosstalk and Human Disease

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these "omics" are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.

Immune monitoring using mass cytometry and related high-dimensional imaging approaches

The cellular complexity and functional diversity of the human immune system necessitate the use of high-dimensional single-cell tools to uncover its role in multifaceted diseases such as rheumatic diseases, as well as other autoimmune and inflammatory disorders. Proteomic technologies that use elemental (heavy metal) reporter ions, such as mass cytometry (also known as CyTOF) and analogous high-dimensional imaging approaches (including multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC)), have been developed from their low-dimensional counterparts, flow cytometry and immunohistochemistry, to meet this need. A growing number of studies have been published that use these technologies to identify functional biomarkers and therapeutic targets in rheumatic diseases, but the full potential of their application to rheumatic disease research has yet to be fulfilled. This Review introduces the underlying technologies for high-dimensional immune monitoring and discusses aspects necessary for their successful implementation, including study design principles, analytical tools and future developments for the field of rheumatology.

Extracting Biological Insight from Untargeted Lipidomics Data

Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community's ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.