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

Chronic lead poisoning-induced budgerigar liver damage, gut microbiota dysbiosis, and metabolic disorder

Birds are sensitive to heavy metal pollution, and lead (Pb) contamination can negatively affect their liver and gut. Therefore, we used budgerigars to examine liver and gut toxicosis caused by Pb exposure in bird, and the possible toxic mechanisms. The findings showed Pb exposure increased liver weight and decreased body weight. Moreover, histopathological and immunofluorescence assay results demonstrated obvious liver damage and cell apoptosis increased in Pb- treated budgerigars. Quantitative polymerase chain reaction (qPCR) results also showed Pb caused an increase in apoptosis by inhibiting the PPAR-γ/PI3K/Akt pathway. The gut microbe analyses indicated Firmicutes, Proteobacteria, and Bacteroidetes were dominant microbial phyla, and Network analysis results shown Arthrobacter, Bradyrhizobium and Alloprevotella as the hubs of Modules I, II, and III, respectively. Phenylpropanoids and polyketides, Organoheterocyclic compounds, Organic oxygen compounds, and Organic nitrogen compounds were dominant metabolite superclasses. Tauroursodeoxycholic acid, taurochenodeoxycholic acid (sodium salt), and 2-[2-(5-bromo-2-pyridyl)diaz-1-enyl]-5-(diethylamino)phenol were significantly enriched in the Pb-treated group. It showed that 41 Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues and 183 pathways differed between the Pb-treated and control budgerigars using microbial and metabolomic data. Moreover, orthogonal partial least-squares discrimination analysis (OPLS-DA) based on microbial and metabolite indicated distinct clusters in the Pb-treated and control groups. Additionally, the correlation analysis results indicated that a positive correlation for the Pb-treated and control groups between gut microbiota and metabolomic data, respectively. Furthermore, the microenvironment of the gut and liver were found to affect each other, and this study demonstrated heavy metal especially Pb may pose serious health risks to birds through the "gut-liver axis" too.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

A universal plasma metabolites-derived signature predicts cardiovascular disease risk in MAFLD

BACKGROUND

Metabolic associated fatty liver disease (MAFLD) is a novel concept proposed in 2020, which is more practical for identifying patients with fatty liver disease with high risk of disease progression. Fatty liver is a driver for extrahepatic complications, particularly cardiovascular diseases (CVD). Although the risk of CVD in MAFLD could be predicted by carotid ultrasound test, a very early stage prediction method before the formation of pathological damage is still lacking.

METHODS

Stool microbiomes and plasma metabolites were compared across 196 well-characterized participants encompassing normal controls, simple MAFLD patients, MAFLD patients with carotid artery pathological changes, and MAFLD patients with diagnosed coronary artery disease (CAD). 16S rDNA sequencing data and untargeted metabolomic profiles were interrogatively analyzed using differential abundance analysis and random forest (RF) machine learning algorithm to identify discriminatory gut microbiomes and metabolomic.

RESULTS

Characteristic microbial changes in MAFLD patients with CVD risk were represented by the increase of Clostridia and Firmicutes-to-Bacteroidetes ratios. Faecalibacterium was negatively correlated with mean-intima-media thickness (IMT), TC, and TG. Megamonas, Bacteroides, Parabacteroides, and Escherichia were positively correlated with the exacerbation of pathological indexes. MAFLD patients with CVD risk were characterized by the decrease of lithocholic acid taurine conjugate, and the increase of ethylvanillin propylene glycol acetal, both of which had close relationship with Ruminococcus and Gemmiger. Biotin l-sulfoxide had positive correlation with mean-IMT, TG, and weight. The general auxin pesticide beta-naphthoxyacetic acid and the food additive glucosyl steviol were both positively correlated with the increase of mean-IMT. The model combining the metabolite signatures with 9 clinical parameters accurately distinguished MAFLD with CVD risk in the proband and validation cohort. It was found that citral was the most important discriminative metabolite marker, which was validated by both in vitro and in vivo experiments.

CONCLUSIONS

Simple MAFLD patients and MAFLD patients with CVD risk had divergent gut microbes and plasma metabolites. The predictive model based on metabolites and 9 clinical parameters could effectively discriminate MAFLD patients with CVD risk at a very early stage.

Integrative metagenomic and metabolomic analyses reveal the potential of gut microbiota to exacerbate acute pancreatitis

Early dysbiosis in the gut microbiota may contribute to the severity of acute pancreatitis (AP), however, a comprehensive understanding of the gut microbiome, potential pathobionts, and host metabolome in individuals with AP remains elusive. Hence, we employed fecal whole-metagenome shotgun sequencing in 82 AP patients and 115 matched healthy controls, complemented by untargeted serum metabolome and lipidome profiling in a subset of participants. Analyses of the gut microbiome in AP patients revealed reduced diversity, disrupted microbial functions, and altered abundance of 77 species, influenced by both etiology and severity. AP-enriched species, mostly potential pathobionts, correlated positively with host liver function and serum lipid indicators. Conversely, many AP-depleted species were short-chain fatty acid producers. Gut microflora changes were accompanied by shifts in the serum metabolome and lipidome. Specifically, certain gut species, like enriched Bilophila wadsworthia and depleted Bifidobacterium spp., appeared to contribute to elevated triglyceride levels in biliary or hyperlipidemic AP patients. Through culturing and whole-genome sequencing of bacterial isolates, we identified virulence factors and clinically relevant antibiotic resistance in patient-derived strains, suggesting a predisposition to opportunistic infections. Finally, our study demonstrated that gavage of specific pathobionts could exacerbate pancreatitis in a caerulein-treated mouse model. In conclusion, our comprehensive analysis sheds light on the gut microbiome and serum metabolome in AP, elucidating the role of pathobionts in disease progression. These insights offer valuable perspectives for etiologic diagnosis, prevention, and intervention in AP and related conditions.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

BACKGROUND

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

METHODS

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

RESULTS

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

CONCLUSIONS

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Strategies for tailoring functional microbial synthetic communities

Natural ecosystems harbor a huge reservoir of taxonomically diverse microbes that are important for plant growth and health. The vast diversity of soil microorganisms and their complex interactions make it challenging to pinpoint the main players important for the life support functions microbes can provide to plants, including enhanced tolerance to (a)biotic stress factors. Designing simplified microbial synthetic communities (SynComs) helps reduce this complexity to unravel the molecular and chemical basis and interplay of specific microbiome functions. While SynComs have been successfully employed to dissect microbial interactions or reproduce microbiome-associated phenotypes, the assembly and reconstitution of these communities have often been based on generic abundance patterns or taxonomic identities and co-occurrences but have only rarely been informed by functional traits. Here, we review recent studies on designing functional SynComs to reveal common principles and discuss multidimensional approaches for community design. We propose a strategy for tailoring the design of functional SynComs based on integration of high-throughput experimental assays with microbial strains and computational genomic analyses of their functional capabilities.

A Review on the Protective Effects of Probiotics against Alzheimer's Disease

This review summarizes the protective effects of probiotics against Alzheimer's disease (AD), one of the most common neurodegenerative disorders affecting older adults. This disease is characterized by the deposition of tau and amyloid β peptide (Aβ) in different parts of the brain. Symptoms observed in patients with AD include struggles with writing, speech, memory, and knowledge. The gut microbiota reportedly plays an important role in brain functioning due to its bidirectional communication with the gut via the gut-brain axis. The emotional and cognitive centers in the brain are linked to the functions of the peripheral intestinal system via this gut-brain axis. Dysbiosis has been linked to neurodegenerative disorders, indicating the significance of gut homeostasis for proper brain function. Probiotics play an important role in protecting against the symptoms of AD as they restore gut-brain homeostasis to a great extent. This review summarizes the characteristics, status of gut-brain axis, and significance of gut microbiota in AD. Review and research articles related to the role of probiotics in the treatment of AD were searched in the PubMed database. Recent studies conducted using animal models were given preference. Recent clinical trials were searched for separately. Several studies conducted on animal and human models clearly explain the benefits of probiotics in improving cognition and memory in experimental subjects. Based on these studies, novel therapeutic approaches can be designed for the treatment of patients with AD.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

Background

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

Methods

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

Results

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules, and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and apoE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

Conclusions

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Exercise and microbiome: From big data to therapy

Exercise is a vital component in maintaining optimal health and serves as a prospective therapeutic intervention for various diseases. The human microbiome, comprised of trillions of microorganisms, plays a crucial role in overall health. Given the advancements in microbiome research, substantial databases have been created to decipher the functionality and mechanisms of the microbiome in health and disease contexts. This review presents an initial overview of microbiomics development and related databases, followed by an in-depth description of the multi-omics technologies for microbiome. It subsequently synthesizes the research pertaining to exercise-induced modifications of the microbiome and diseases that impact the microbiome. Finally, it highlights the potential therapeutic implications of an exercise-modulated microbiome in intestinal disease, obesity and diabetes, cardiovascular disease, and immune/inflammation-related diseases.

Unraveling the Gut Microbiome-Diet Connection: Exploring the Impact of Digital Precision and Personalized Nutrition on Microbiota Composition and Host Physiology

The human gut microbiome, an intricate ecosystem housing trillions of microorganisms within the gastrointestinal tract, holds significant importance in human health and the development of diseases. Recent advances in technology have allowed for an in-depth exploration of the gut microbiome, shedding light on its composition and functions. Of particular interest is the role of diet in shaping the gut microbiome, influencing its diversity, population size, and metabolic functions. Precision nutrition, a personalized approach based on individual characteristics, has shown promise in directly impacting the composition of the gut microbiome. However, to fully understand the long-term effects of specific diets and food components on the gut microbiome and to identify the variations between individuals, longitudinal studies are crucial. Additionally, precise methods for collecting dietary data, alongside the application of machine learning techniques, hold immense potential in comprehending the gut microbiome's response to diet and providing tailored lifestyle recommendations. In this study, we investigated the complex mechanisms that govern the diverse impacts of nutrients and specific foods on the equilibrium and functioning of the individual gut microbiome of seven volunteers (four females and three males) with an average age of 40.9 ± 10.3 years, aiming at identifying potential therapeutic targets, thus making valuable contributions to the field of personalized nutrition. These findings have the potential to revolutionize the development of highly effective strategies that are tailored to individual requirements for the management and treatment of various diseases.

Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children

Aims/hypothesis

Appearance of multiple islet cell autoantibodies in early life is indicative of future progression to overt type 1 diabetes, however, at varying rates. Here, we aimed to study whether distinct metabolic patterns could be identified in rapid progressors (RP, disease manifestation within 18 months after the initial seroconversion to autoantibody positivity) vs. slow progressors (SP, disease manifestation at 60 months or later from the appearance of the first autoantibody).

Methods

Longitudinal samples were collected from RP (n=25) and SP (n=41) groups at the ages of 3, 6, 12, 18, 24, or ≥ 36 months. We performed a comprehensive metabolomics study, analyzing both polar metabolites and lipids. The sample series included a total of 239 samples for lipidomics and 213 for polar metabolites.

Results

We observed that metabolites mediated by gut microbiome, such as those involved in tryptophan metabolism, were the main discriminators between RP and SP. The study identified specific circulating molecules and pathways, including amino acid (threonine), sugar derivatives (hexose), and quinic acid that may define rapid vs. slow progression to type 1 diabetes. However, the circulating lipidome did not appear to play a major role in differentiating between RP and SP.

Conclusion/interpretation

Our study suggests that a distinct metabolic profile is linked with the type 1 diabetes progression. The identification of specific metabolites and pathways that differentiate RP from SP may have implications for early intervention strategies to delay the development of type 1 diabetes.

Resistant starch decreases intrahepatic triglycerides in patients with NAFLD via gut microbiome alterations

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. To investigate the effects of resistant starch (RS) as a microbiota-directed dietary supplement for NAFLD treatment, we coupled a 4-month randomized placebo-controlled clinical trial in individuals with NAFLD (ChiCTR-IOR-15007519) with metagenomics and metabolomics analysis. Relative to the control (n = 97), the RS intervention (n = 99) resulted in a 9.08% absolute reduction of intrahepatic triglyceride content (IHTC), which was 5.89% after adjusting for weight loss. Serum branched-chain amino acids (BCAAs) and gut microbial species, in particular Bacteroides stercoris, significantly correlated with IHTC and liver enzymes and were reduced by RS. Multi-omics integrative analyses revealed the interplay among gut microbiota changes, BCAA availability, and hepatic steatosis, with causality supported by fecal microbiota transplantation and monocolonization in mice. Thus, RS dietary supplementation might be a strategy for managing NAFLD by altering gut microbiota composition and functionality.

A general framework for powerful confounder adjustment in omics association studies

MOTIVATION

Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction.

RESULTS

This study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications.

AVAILABILITY AND IMPLEMENTATION

R codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.

Metabolic Profiling Early Post-Allogeneic Haematopoietic Cell Transplantation in the Context of CMV Infection

Immune dysfunction resulting from allogeneic haematopoietic stem cell transplantation (aHSCT) predisposes one to an elevated risk of cytomegalovirus (CMV) infection. Changes in metabolism have been associated with adverse outcomes, and in this study, we explored the associations between metabolic profiles and post-transplantation CMV infection using plasma samples collected 7-33 days after aHSCT. We included 68 aHSCT recipients from Rigshospitalet, Denmark, 50% of whom experienced CMV infection between days 34-100 post-transplantation. First, we investigated whether 12 metabolites selected based on the literature were associated with an increased risk of post-transplantation CMV infection. Second, we conducted an exploratory network-based analysis of the complete metabolic and lipidomic profiles in relation to clinical phenotypes and biological pathways. Lower levels of trimethylamine N-oxide were associated with subsequent CMV infection (multivariable logistic regression: OR = 0.63; 95% CI = [0.41; 0.87]; p = 0.01). Explorative analysis revealed 12 clusters of metabolites or lipids, among which one was predictive of CMV infection, and the others were associated with conditioning regimens, age upon aHSCT, CMV serostatus, and/or sex. Our results provide evidence for an association between the metabolome and CMV infection post-aHSCT that is independent of known risk factors.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

A radiomics model development via the associations with genomics features in predicting axillary lymph node metastasis of breast cancer: a study based on a public database and single-centre verification

AIM

To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients with breast cancer.

MATERIALS AND METHODS

Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics-genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis.

RESULTS

After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve [AUC] = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases.

CONCLUSION

The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework.

Key Points

The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.

Longitudinal multi-omics analyses of the gut-liver axis reveals metabolic dysregulation in hepatitis C infection and cirrhosis

The gut and liver are connected via the portal vein, and this relationship, which includes the gut microbiome, is described as the gut-liver axis. Hepatitis C virus (HCV) can infect the liver and cause fibrosis with chronic infection. HCV has been associated with an altered gut microbiome; however, how these changes impact metabolism across the gut-liver axis and how this varies with disease severity and time is unclear. Here we used multi-omics analysis of portal and peripheral blood, faeces and liver tissue to characterize the gut-liver axis of patients with HCV across a fibrosis severity gradient before (n = 29) and 6 months after (n = 23) sustained virologic response, that is, no detection of the virus. Fatty acids were the major metabolites perturbed across the liver, portal vein and gut microbiome in HCV, especially in patients with cirrhosis. Decreased fatty acid degradation by hepatic peroxisomes and mitochondria was coupled with increased free fatty acid (FFA) influx to the liver via the portal vein. Metatranscriptomics indicated that Anaerostipes hadrus-mediated fatty acid synthesis influences portal FFAs. Both microbial fatty acid synthesis and portal FFAs were associated with enhanced hepatic fibrosis. Bacteroides vulgatus-mediated intestinal glycan breakdown was linked to portal glycan products, which in turn correlated with enhanced portal inflammation in HCV. Paired comparison of patient samples at both timepoints showed that hepatic metabolism, especially in peroxisomes, is persistently dysregulated in cirrhosis independently of the virus. Sustained virologic response was associated with a potential beneficial role for Methanobrevibacter smithii, which correlated with liver disease severity markers. These results develop our understanding of the gut-liver axis in HCV and non-HCV liver disease aetiologies and provide a foundation for future therapies.

Integrative metagenomic and metabolomic analyses reveal gut microbiota-derived multiple hits connected to development of gestational diabetes mellitus in humans

Gestational diabetes mellitus (GDM) is characterized by the development of hyperglycemia and insulin resistance during the second or third trimester of pregnancy, associated with considerable risks to both the mother and developing fetus. Although emerging evidence suggests an association between the altered gut microbiota and GDM, remarkably little is known about the microbial and metabolic mechanisms that link the dysbiosis of the gut microbiota to the development of GDM. In this study, a metagenome-wide association study and serum metabolomics profiling were performed in a cohort of pregnant women with GDM and pregnant women with normal glucose tolerance (NGT). We identified gut microbial alterations associated with GDM and linked to the changes in circulating metabolites. Blood metabolite profiles revealed that GDM patients exhibited a marked increase in 2-hydroxybutyric acid and L-alpha-aminobutyric acid, but a decrease in methionine sulfoxide, allantoin, and dopamine and dopaminergic synapse, when compared with those in NGT controls. Short-chain fatty acid-producing genera, including Faecalibacterium, Prevotella, and Streptococcus, and species Bacteroides coprophilus, Eubacterium siraeum, Faecalibacterium prausnitzii, Prevotella copri, and Prevotella stercorea, were significantly reduced in GDM patients relative to those in NGT controls. Bacterial co-occurrence network analysis revealed that pro-inflammatory bacteria were over-represented as the core species in GDM patients. These microbial and metabolic signatures are closely associated with clinical parameters of glucose metabolism in GDM patients and NGT controls. In conclusion, we identified circulating dopamine insufficiency, imbalanced production of SCFAs, and excessive metabolic inflammation as gut microbiota-driven multiple parallel hits linked to GDM development. This work might explain in part the mechanistic link between altered gut microbiota and GDM pathogenesis, and suggest that gut microbiota may serve as a promising target to intervene in GDM.

Altered Gut Microbial Profile Accompanied by Abnormal Fatty Acid Metabolism Activity Exacerbates Endometrial Cancer Progression

Endometrial cancer (EC) is the most prevalent gynecological malignancy, with a higher risk in obese woman, indicating the possibility of gut microbiota involvement in EC progression. However, no direct evidence of a relationship between EC and gut microbiota in humans has been discovered. Here, we performed 16S rRNA sequencing to explore the relationship between dysbiosis of gut microbiota and cancer development in different types of EC patients. The results clearly show the differential profiles of gut microbiota between EC patients and normal participants as well as the association between gut microbiota and EC progression. Targeted metabolomics of plasma revealed an increased level of C16:1 and C20:2, which was positively associated with the abundance of Ruminococcus sp. N15.MGS-57. The higher richness of Ruminococcus sp. N15.MGS-57 in EC subjects not only was positively associated with blood C16:1 and C20:2 but also was negatively correlated with betalain and indole alkaloid biosynthesis. Furthermore, the combined marker panel of gut bacteria, blood metabolites, and clinical indices could distinguish the EC patients under lean and overweight conditions from normal subjects with high accuracy in both discovery and validation sets. In addition, the alteration of tumor microenvironment metabolism of EC was characterized by imaging mass microscopy. Spatial visualization of fatty acids showed that C16:1 and C18:1 obviously accumulate in tumor tissue, and C16:1 may promote EC cell invasion and metastasis through mTOR signaling. The aberrant fecal microbiome, more specifically, Ruminococcus sp. N15.MGS-57 and spatially distributed C16:1 in EC tissues, can be used as a biomarker of clinical features and outcomes and provide a new therapeutic target for clinical treatment. IMPORTANCE A growing number of studies have shown the connection between gut microbiota, obesity, and cancer. However, to our knowledge, the association between gut microbiota and endometrial cancer progression in humans has not been studied. We recruited EC and control individuals as research participants and further subgrouped subjects by body mass index to examine the association between gut microbiota, metabolites, and clinical indices. The higher richness of Ruminococcus sp. N15.MGS-57 in EC subjects was not only positively associated with blood C16:1 but also negatively correlated with betalain and indole alkaloid biosynthesis. Spatial visualization of fatty acids by imaging mass microscopy showed that C16:1 obviously accumulates in tumor tissue, and C16:1 may promote the EC cell invasion and metastasis through mTOR signaling. The aberrant fecal microbiome, more specifically, Ruminococcus sp. N15.MGS-57 and spatially distributed C16:1, can be used as a biomarker of clinical features and outcomes and provide a new therapeutic target for clinical treatment.

Probiotics protect against hepatic steatosis in tris (2-chloroethyl) phosphate-induced metabolic disorder of mice via FXR signaling

Tris (2-chloroethyl) phosphate (TCEP), the most widely useful and most frequently detective organophosphate flame retardants in environment, has been shown potential relationship with adolescent weight. Probiotics is an effective therapy for metabolic diseases such as obesity and NAFLD with gut microbiota dysregulation. This study aims to explore the protective effects of probiotics against lipid metabolic disorder induced by chronic TCEP exposure and demonstrate the mechanism of this event. The data showed that dietary complex probiotics supplement attenuated TCEP-induced obesity, hyperlipidemia, liver dysfunction, and hepatic steatosis. In addition, dietary complex probiotics suppressed TCEP-promoted ileal FXR signaling, and upregulated hepatic FXR/SHP pathway inhibited by TCEP. Moreover, dietary complex probiotics stimulated PPARα-mediated lipid oxidation and suppressed SREBP1c/PPARγ-mediated lipid synthesis via regulation of FXR signaling. Therefore, this study indicates that dietary complex probiotics could protect against hepatic steatosis via FXR-mediated signaling pathway in TCEP-induced metabolism disorder in mice, resulting in attenuation of systemic lipid accumulation.

Metagenomic evidence of suppressed methanogenic pathways along soil profile after wetland conversion to cropland

Wetland conversion to cropland substantially suppresses methane (CH4) emissions due to the strong suppression of methanogenesis, which consists of various pathways. In this study, we evaluated the cultivation impacts on four predominant CH4 production pathways, including acetate, carbon dioxide (CO2), methylamines, and methanol, in a wetland and cultivated cropland in northeastern China. The results showed significant suppression of CH4 production potential and the abundance of genes for all four methanogenic pathways in cropland. The consistency between CH4 production and methanogenesis genes indicates the robustness of genomic genes in analyzing methanogenesis. The suppression effects varied across seasons and along soil profiles, most evident in spring and 0 to 30 cm layers. The acetate pathway accounted for 55% in wetland vs. 70% in the cropland of all functional genes for CH4 production; while the other three pathways were stronger in response to cultivation, which presented as stronger suppressions in both abundance of functional genes (declines are 52% of CO2 pathway, 68% of methanol pathway, and 62% of methylamines pathway, vs. 19% of acetate pathway) and their percentages in four pathways (from 20 to 15% for CO2, 15 to 9% for methylamines, and 10 to 6% for methanol pathway vs. 55 to 70% for acetate pathway). The structural equation models showed that substrate availability was most correlated with CH4 production potential in the wetland, while the positive correlations of acetate, CO2, and methylamine pathways with CH4 production potential were significant in the cropland. The quantitative responses of four CH4 production pathways to land conversion reported in this study provide benchmark information for validating the CH4 model in simulating CH4 cycling under land use and land cover change.

Integrated multi-omics reveal important roles of gut contents in intestinal ischemia–reperfusion induced injuries in rats

Intestinal ischemia–reperfusion (IIR) is a life-threatening clinical event with damaging signals whose origin and contents are unclear. Here we observe that IIR significantly affect the metabolic profiles of most organs by unbiased organ-wide metabolic analysis of gut contents, blood, and fifteen organs in rats (n = 29). Remarkably, correlations between gut content metabolic profiles and those of other organs are the most significant. Gut contents are also the only ones to show dynamic correlations during IIR. Additionally, according to targeted metabolomics analysis, several neurotransmitters are considerably altered in the gut during IIR, and displayed noteworthy correlations with remote organs. Likewise, metagenomics analysis (n = 35) confirm the effects of IIR on gut microbiota, and identify key species fundamental to the changes in gut metabolites, particularly neurotransmitters. Our multi-omics results establish key roles of gut contents in IIR induced remote injury and provide clues for future exploration.

Die Dai et al. evaluate changes in the metabolomic and gut microbiome in response to experimental intestinal ischemia reperfusion (IIR) injury in rats. Their results provide further insight into how gut contents contribute to widespread injury in IIR patients.

Association of the characteristics of the blood metabolome and gut microbiome with the outcome of methotrexate therapy in psoriasis

Metabolic status and gut microecology are implicated in psoriasis. Methotrexate (MTX) is usually the first-line treatment for this disease. However, the relationship between MTX and host metabolic status and the gut microbiota is unclear. This study aimed to characterize the features of blood metabolome and gut microbiome in patients with psoriasis after treatment with MTX. Serum and stool samples were collected from 15 patients with psoriasis. Untargeted liquid chromatography–mass spectrometry and metagenomics sequencing were applied to profile the blood metabolome and gut microbiome, respectively. We found that the response to MTX varied according to metabolomic and metagenomic features at baseline; for example, patients who had high levels of serum nutrient molecular and more enriched gut microbiota had a poor response. After 16 weeks of MTX, we observed a reduction in microbial activity pathways, and patients with a good response showed more microbial activity and less biosynthesis of serum fatty acid. We also found an association between the serum metabolome and the gut microbiome before intervention with MTX. Carbohydrate metabolism, transporter systems, and protein synthesis within microbes were associated with host metabolic clusters of lipids, benzenoids, and organic acids. These findings suggest that the metabolic status of the blood and the gut microbiome is involved in the effectiveness of MTX in psoriasis, and that inhibition of symbiotic intestinal microbiota may be one of the mechanisms of action of MTX. Prospective studies in larger sample sizes are needed to confirm these findings.

Meals, Microbiota and Mental Health in Children and Adolescents (MMM-Study): A protocol for an observational longitudinal case-control study

Recent studies indicate that the interplay between diet, intestinal microbiota composition, and intestinal permeability can impact mental health. More than 10% of children and adolescents in Iceland suffer from mental disorders, and rates of psychotropics use are very high. The aim of this novel observational longitudinal case-control study, “Meals, Microbiota and Mental Health in Children and Adolescents (MMM-Study)” is to contribute to the promotion of treatment options for children and adolescents diagnosed with mental disorders through identification of patterns that may affect the symptoms. All children and adolescents, 5–15 years referred to the outpatient clinic of the Child and Adolescent Psychiatry Department at The National University Hospital in Reykjavik, Iceland, for one year (n≈150) will be invited to participate. There are two control groups, i.e., sex-matched children from the same postal area (n≈150) and same parent siblings (full siblings) in the same household close in age +/- 3 years (n<150). A three-day food diary, rating scales for mental health, and multiple questionnaires will be completed. Biosamples (fecal-, urine-, saliva-, blood samples, and buccal swab) will be collected and used for 16S rRNA gene amplicon sequencing of the oral and gut microbiome, measurements of serum factors, quantification of urine metabolites and host genotype, respectively. For longitudinal follow-up, data collection will be repeated after three years in the same groups. Integrative analysis of diet, gut microbiota, intestinal permeability, serum metabolites, and mental health will be conducted applying bioinformatics and systems biology approaches. Extensive population-based data of this quality has not been collected before, with collection repeated in three years’ time, contributing to the high scientific value. The MMM-study follows the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) guidelines. Approval has been obtained from the Icelandic National Bioethics Committee, and the study is registered with Clinicaltrials.gov. The study will contribute to an improved understanding of the links between diet, gut microbiota and mental health in children through good quality study design by collecting information on multiple components, and a longitudinal approach. Furthermore, the study creates knowledge on possibilities for targeted and more personalized dietary and lifestyle interventions in subgroups.

Trial registration numbers: VSN-19-225 & NCT04330703.

Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions

The mechanistic role of the airway microbiome in chronic obstructive pulmonary disease (COPD) remains largely unexplored. We present a landscape of airway microbe-host interactions in COPD through an in-depth profiling of the sputum metagenome, metabolome, host transcriptome and proteome from 99 patients with COPD and 36 healthy individuals in China. Multi-omics data were integrated using sequential mediation analysis, to assess in silico associations of the microbiome with two primary COPD inflammatory endotypes, neutrophilic or eosinophilic inflammation, mediated through microbial metabolic interaction with host gene expression. Hypotheses of microbiome-metabolite-host interaction were identified by leveraging microbial genetic information and established metabolite-human gene pairs. A prominent hypothesis for neutrophil-predominant COPD was altered tryptophan metabolism in airway lactobacilli associated with reduced indole-3-acetic acid (IAA), which was in turn linked to perturbed host interleukin-22 signalling and epithelial cell apoptosis pathways. In vivo and in vitro studies showed that airway microbiome-derived IAA mitigates neutrophilic inflammation, apoptosis, emphysema and lung function decline, via macrophage-epithelial cell cross-talk mediated by interleukin-22. Intranasal inoculation of two airway lactobacilli restored IAA and recapitulated its protective effects in mice. These findings provide the rationale for therapeutically targeting microbe-host interaction in COPD.

Effects of an Amino Acid-Based Formula Supplemented with Two Human Milk Oligosaccharides on Growth, Tolerability, Safety, and Gut Microbiome in Infants with Cow's Milk Protein Allergy

This open-label, non-randomized, multicenter trial (Registration: NCT03661736) aimed to assess if an amino acid-based formula (AAF) supplemented with two human milk oligosaccharides (HMO) supports normal growth and is well tolerated in infants with a cow's milk protein allergy (CMPA). Term infants aged 1-8 months with moderate-to-severe CMPA were enrolled. The study formula was an AAF supplemented with 2'-fucosyllactose (2'-FL) and lacto-N-neotetraose (LNnT). Infants were fed the study formula for 4 months and were offered to remain on the formula until 12 months of age. Tolerance and safety were assessed throughout the trial. Out of 32 infants (mean age 18.6 weeks; 20 (62.5%) male), 29 completed the trial. During the 4-month principal study period, the mean weight-for-age Z score (WAZ) increased from -0.31 at the baseline to +0.28 at the 4-months' follow-up. Linear and head growth also progressed along the WHO child growth reference, with a similar small upward trend. The formula was well tolerated and had an excellent safety profile. When comparing the microbiome at the baseline to the subsequent visits, there was a significant on-treatment enrichment in HMO-utilizing bifidobacteria, which was associated with a significant increase in fecal short-chain fatty acids. In addition, we observed a significant reduction in the abundance of fecal Proteobacteria, suggesting that the HMO-supplemented study formula partially corrected the gut microbial dysbiosis in infants with CMPA.

Intrapopulation adaptive variance supports thermal tolerance in a reef-building coral

Coral holobionts are multi-species assemblages, which adds significant complexity to genotype-phenotype connections underlying ecologically important traits like coral bleaching. Small scale heterogeneity in bleaching is ubiquitous in the absence of strong environmental gradients, which provides adaptive variance needed for the long-term persistence of coral reefs. We used RAD-seq, qPCR and LC-MS/MS metabolomics to characterize host genomic variation, symbiont community and biochemical correlates in two bleaching phenotypes of the vertically transmitting coral Montipora capitata. Phenotype was driven by symbiosis state and host genetic variance. We documented 5 gene ontologies that were significantly associated with both the binary bleaching phenotype and symbiont composition, representing functions that confer a phenotype via host-symbiont interactions. We bred these corals and show that symbiont communities were broadly conserved in bulk-crosses, resulting in significantly higher survivorship under temperature stress in juveniles, but not larvae, from tolerant parents. Using a select and re-sequence approach, we document numerous gene ontologies selected by heat stress, some of which (cell signaling, antioxidant activity, pH regulation) have unique selection dynamics in larvae from thermally tolerant parents. These data show that vertically transmitting corals may have an adaptive advantage under climate change if host and symbiont variance interact to influence bleaching phenotype.

Selective breeding of corals with different bleaching phenotypes demonstrates the potential for climate adaptation in vertically transmitting species.

Altered faecal microbiome and metabolome in IgG4-related sclerosing cholangitis and primary sclerosing cholangitis

OBJECTIVE

Multiple clinical similarities exist between IgG4-related sclerosing cholangitis (IgG4-SC) and primary sclerosing cholangitis (PSC), and while gut dysbiosis has been extensively studied in PSC, the role of the gut microbiota in IgG4-SC remains unknown. Herein, we aimed to evaluate alterations of the gut microbiome and metabolome in IgG4-SC and PSC.

DESIGN

We performed 16S rRNA gene amplicon sequencing of faecal samples from 135 subjects with IgG4-SC (n=34), PSC (n=37) and healthy controls (n=64). A subset of the samples (31 IgG4-SC, 37 PSC and 45 controls) also underwent untargeted metabolomic profiling.

RESULTS

Compared with controls, reduced alpha-diversity and shifted microbial community were observed in IgG4-SC and PSC. These changes were accompanied by differences in stool metabolomes. Importantly, despite some common variations in the microbiota composition and metabolic activity, integrative analyses identified distinct host-microbe associations in IgG4-SC and PSC. The disease-associated genera and metabolites tended to associate with the transaminases in IgG4-SC. Notable depletion of Blautia and elevated succinic acid may underlie hepatic inflammation in IgG4-SC. In comparison, potential links between the microbial or metabolic signatures and cholestatic parameters were detected in PSC. Particularly, concordant decrease of Eubacterium and microbiota-derived metabolites, including secondary bile acids, implicated novel host-microbial metabolic pathways involving cholestasis of PSC. Interestingly, the predictive models based on metabolites were more effective in discriminating disease status than those based on microbes.

CONCLUSIONS

Our data reveal that IgG4-SC and PSC possess divergent host-microbe interplays that may be involved in disease pathogenesis. These data emphasise the uniqueness of IgG4-SC.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

MOTIVATION

Recent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.

RESULTS

We present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.

AVAILABILITY AND IMPLEMENTATION

MIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

MOTIVATION

The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.

RESULTS

Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies.

AVAILABILITYAND IMPLEMENTATION

3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The microbial metabolome in metabolic-associated fatty liver disease

Metabolism-associated fatty liver disease (MAFLD) is defined as the presence of excess fat in the liver in the absence of excess alcohol consumption and metabolic dysfunction. It has also been described as the hepatic manifestation of metabolic syndrome. The incidence of MAFLD has been reported to be 43-60% in diabetics, ~90% in patients with hyperlipidemia, and 91% in morbidly obese patients. Risk factors that have been associated with the development of MAFLD include male gender, increasing age, obesity, insulin resistance, diabetes, and hyperlipidemia. All of these risk factors have been linked to alterations of the gut microbiota, that is, gut dysbiosis. MAFLD can progress to non-alcoholic steatohepatitis with the presence of inflammation and ballooning, which can deteriorate into cirrhosis, MAFLD-related hepatocellular carcinoma, and liver failure. In this review, we will be focused on the role of the gut microbial metabolome in the development, progression, and potential treatment of MAFLD.

Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma

Asthma is a multifactorial inflammatory disorder of the respiratory system characterized by high diversity in clinical manifestations, underlying pathological mechanisms and response to treatment. It is generally established that human microbiota plays an essential role in shaping a healthy immune response, while its perturbation can cause chronic inflammation related to a wide range of diseases, including asthma. Systems biology approaches encompassing microbiome analysis can offer valuable platforms towards a global understanding of asthma complexity and improving patients' classification, status monitoring and therapeutic choices. In the present review, we summarize recent studies exploring the contribution of microbiota dysbiosis to asthma pathogenesis and heterogeneity in the context of asthma phenotypes-endotypes and administered medication. We subsequently focus on emerging efforts to gain deeper insights into microbiota-host interactions driving asthma complexity by integrating microbiome and host multi-omics data. One of the most prominent achievements of these research efforts is the association of refractory neutrophilic asthma with certain microbial signatures, including predominant pathogenic bacterial taxa (such as Proteobacteria phyla, Gammaproteobacteria class, especially species from Haemophilus and Moraxella genera). Overall, despite existing challenges, large-scale multi-omics endeavors may provide promising biomarkers and therapeutic targets for future development of novel microbe-based personalized strategies for diagnosis, prevention and/or treatment of uncontrollable asthma.

Human gut microbiome impacts skeletal muscle mass via gut microbial synthesis of the short-chain fatty acid butyrate among healthy menopausal women

BACKGROUND

Increasing evidence suggests that human gut microbiome plays an important role in variation of skeletal muscle mass (SMM). However, specific causal mechanistic relationship of human gut microbiome with SMM remains largely unresolved. Understanding the causal mechanistic relationship may provide a basis for novel interventions for loss of SMM. This study investigated whether human gut microbiome has a causal effect on SMM among Chinese community-dwelling healthy menopausal women.

METHODS

Estimated SMM was derived from whole-body dual-energy X-ray absorptiometry. We performed integrated analyses on whole-genome sequencing, shotgun metagenomic sequencing, and serum short-chain fatty acids (SCFAs), as well as available host SMM measurements among community-dwelling healthy menopausal women (N = 482). We combined the results with summary statistics from genome-wide association analyses for human gut microbiome (N = 952) and SMM traits (N = 28 330). As a prerequisite for causality, we used a computational protocol that was proposed to measure correlations among gut metagenome, metabolome, and the host trait to investigate the relationship between human gut microbiome and SMM. Causal inference methods were applied to assess the potential causal effects of gut microbial features on SMM, through one-sample and two-sample Mendelian randomization (MR) analyses, respectively.

RESULTS

In metagenomic association analyses, the increased capacity for gut microbial synthesis of the SCFA butyrate was significantly associated with serum butyrate levels [Spearman correlation coefficient (SCC) = 0.13, P = 0.02] and skeletal muscle index (SCC = 0.084, P = 0.002). Of interest was the finding that two main butyrate-producing bacterial species were both positively associated with the increased capacity for gut microbial synthesis of butyrate [Faecalibacterium prausnitzii (SCC = 0.25, P = 6.6 × 10^-7 ) and Butyricimonas virosa (SCC = 0.15, P = 0.001)] and for skeletal muscle index [F. prausnitzii (SCC = 0.16, P = 6.2 × 10^-4 ) and B. virosa (SCC = 0.17, P = 2.4 × 10^-4 )]. One-sample MR results showed a causal effect between gut microbial synthesis of the SCFA butyrate and appendicular lean mass (β = 0.04, 95% confidence interval 0.029 to 0.051, P = 0.003). Two-sample MR results further confirmed the causal effect between gut microbial synthesis of the SCFA butyrate and appendicular lean mass (β = 0.06, 95% confidence interval 0 to 0.13, P = 0.06).

CONCLUSIONS

Our results may help the future development of novel intervention approaches for preventing or alleviating loss of SMM.

Quantitative genome-scale metabolic modeling of human CD4+ T cell differentiation reveals subset-specific regulation of glycosphingolipid pathways

T cell activation, proliferation, and differentiation involve metabolic reprogramming resulting from the interplay of genes, proteins, and metabolites. Here, we aim to understand the metabolic pathways involved in the activation and functional differentiation of human CD4⁺ T cell subsets (T helper [Th]1, Th2, Th17, and induced regulatory T [iTreg] cells). Here, we combine genome-scale metabolic modeling, gene expression data, and targeted and non-targeted lipidomics experiments, together with in vitro gene knockdown experiments, and show that human CD4⁺ T cells undergo specific metabolic changes during activation and functional differentiation. In addition, we confirm the importance of ceramide and glycosphingolipid biosynthesis pathways in Th17 differentiation and effector functions. Through in vitro gene knockdown experiments, we substantiate the requirement of serine palmitoyltransferase (SPT), a de novo sphingolipid pathway in the expression of proinflammatory cytokines (interleukin [IL]-17A and IL17F) by Th17 cells. Our findings provide a comprehensive resource for selective manipulation of CD4⁺ T cells under disease conditions characterized by an imbalance of Th17/natural Treg (nTreg) cells.

Sustained Drug Treatment Alters the Gut Microbiota in Rheumatoid Arthritis

Several studies have investigated the causative role of the microbiome in the development of rheumatoid arthritis (RA), but changes in the gut microbiome in RA patients during drug treatment have been less well studied. Here, we tracked the longitudinal changes in gut bacteria in 22 RA patients who were randomized into two groups and treated with Huayu-Qiangshen-Tongbi formula (HQT) plus methotrexate (MTX) or leflunomide (LEF) plus MTX. There were differences in the gut microbiome between untreated (at baseline) RA patients and healthy controls, with 37 species being more abundant in the RA patients and 21 species (including Clostridium celatum) being less abundant. Regarding the functional analysis, vitamin K2 biosynthesis was associated with RA-enriched bacteria. Additionally, in RA patients, alterations in gut microbial species appeared to be associated with RA-related clinical indicators through changing various gut microbiome functional pathways. The clinical efficacy of the two treatments was further observed to be similar, but the response trends of RA-related clinical indices in the two treatment groups differed. For example, HQT treatment affected the erythrocyte sedimentation rate (ESR), while LEF treatment affected the C-reactive protein (CRP) level. Further, 11 species and 9 metabolic pathways significantly changed over time in the HQT group (including C. celatum, which increased), while only 4 species and 2 metabolic pathways significantly changed over time in the LEF group. In summary, we studied the alterations in the gut microbiome of RA patients being treated with HQT or LEF. The results provide useful information on the role of the gut microbiota in the pathogenesis of RA, and they also provide potentially effective directions for developing new RA treatments.

Effects of cadmium on organ function, gut microbiota and its metabolomics profile in adolescent rats

Cadmium (Cd) exposure in adult animals can result in multi-organ damages and gut microbiota disturbance. However, Cd's consequences on health and gut microbiota during adolescence are obscure. In the present study, three-week-old SD rats were exposed to Cd at doses of 0, 0.25, 1, and 4 mg/kg body weight for eight weeks, and the changes of liver, kidney, and ovary function, as well as gut microbiota and its metabolomics profile, were analyzed. After transplantation of fecal bacteria from the 4 mg/kg Cd-treated group into age-matched rats (4 mg/kg-Cd recipients), the organ function and inflammatory reaction were evaluated. The results indicated that Cd perturbed gut microbiota composition, significantly decreased the abundance of Prevotella and Lachnoclostridium but increased Escherichia coli_Shigella. The fecal metabolome profile was altered and was closely correlated with some specific genera. These changes were accompanied by the inflammatory response, dyslipidemia, kidney dysfunction, and abnormal estrogen level. In 4 mg/kg-Cd recipients, the serum triglyceride (TG), lipopolysaccharide (LPS), and inflammatory cytokines were increased with the expressions of IL-1β, IL-6, TNF-α genes up-regulated in liver and kidney. Overall, this study demonstrated that Cd exposure during adolescence could cause disturbance of gut microbiota, dysfunction of liver, kidney, and ovary, which may be correlated with the activation of Cd-induced inflammatory response.

Metagenomic analysis revealed the potential role of gut microbiome in gout

Emerging evidence indicates an association between gut microbiome and arthritis diseases including gout. However, how and which gut bacteria affect host urate degradation and inflammation in gout remains unclear. Here we performed a metagenome analysis on 307 fecal samples from 102 gout patients and 86 healthy controls. Gout metagenomes significantly differed from those of healthy controls. The relative abundances of Prevotella, Fusobacterium, and Bacteroides were increased in gout, whereas those of Enterobacteriaceae and butyrate-producing species were decreased. Functionally, gout patients had greater abundances for genes in fructose, mannose metabolism and lipid A biosynthesis, and lower for genes in urate degradation and short chain fatty acid production. A three-pronged association between metagenomic species, functions and clinical parameters revealed that decreased abundances of species in Enterobacteriaceae were associated with reduced amino acid metabolism and environmental sensing, which together contribute to increased serum uric acid and C-reactive protein levels in gout. A random forest classifier based on three gut microbial genes showed high predictivity for gout in both discovery and validation cohorts (0.91 and 0.80 accuracy), with high specificity in the context of other chronic disorders. Longitudinal analysis showed that uric-acid-lowering and anti-inflammatory drugs partially restored gut microbiota after 24-week treatment. Comparative analysis with obesity, type 2 diabetes, ankylosing spondylitis and rheumatoid arthritis indicated that gout metagenomes were more similar to those of autoimmune than metabolic diseases. Our results suggest that gut dysbiosis was associated with dysregulated host urate degradation and systemic inflammation and may be used as non-invasive diagnostic markers for gout.

Prebiotic inulin as a treatment of obesity related nonalcoholic fatty liver disease through gut microbiota: a critical review

The microbial-derived products, including short chain fatty acids, lipopolysaccharide and secondary bile acids, have been shown to participate in the regulation of hepatic lipid metabolism. Previous studies have demonstrated that prebiotics, such as oligosaccharide and inulin, have abilities to change the concentration of microbial-derived products through modulating the microbial community structure, thus controlling body weight and alleviating hepatic fat accumulation. However, recent evidence indicates that there are individual differences in host response upon inulin treatment due to the differences in host microbial composition before dietary intervention. Probably it is because of the multiple relationships among bacterial species (e.g., competition and mutualism), which play key roles in the degradation of inulin and the regulation of microbial structure. Thereby, analyzing the composition and function of initial gut microbiota is essential for improving the efficacy of prebiotics supplementation. Furthermore, considering that different structures of polysaccharides can be used by different microorganisms, the chemical structure of processed inulin should be tested before using prebiotic inulin to treat obesity related nonalcoholic fatty liver disease.

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

Different Reactions in Each Enterotype Depending on the Intake of Probiotic Yogurt Powder

Probiotics can be used as a nutritional strategy to improve gut homeostasis. We aimed to evaluate the intestinal microbiota profile of 18 subjects after ingestion of probiotic yogurt powder (PYP) based on enterotype. The subjects were classified into three enterotypes according to their microbial community: Bacteroides (n = 9, type B), Prevotella (n = 3, type P), and Ruminococcus (n = 6, type R). We performed controlled termination in a transient series that included a control period of three weeks before probiotic intake, PYP intake for three weeks, and a three-week washout period. Fecal microbiota composition was analyzed by sequencing the V3-V4 super variable region of 16S rRNA. Based on the Bristol stool shape scale, abnormal stool shape improved with PYP intake, and bowel movements were activated. The abundance of Faecalibacterium, Eggerthella, and Leuconostoc, which ferment and metabolize glucose, showed a strong correlation with type B Bacteroides, and glucose metabolism improvement was observed in all type B subjects. Alkaline phosphatase was significantly improved only in type B. In addition, the abundance of type B Bacteroides showed a negative correlation with that of Lactobacillus. The abundance of Streptococcus, Agathobacter, and Christensenella, which are involved in lipid metabolism, showed a strong correlation with that of type P Prevotella, and triglyceride metabolism improvement was observed in all type P subjects. The gut microbiota showed only short-term changes after PYP intake and showed resilience by returning to its original state when PYP intake was interrupted. In summary, the different responses to PYP intake may result from the different enterotypes and associated strains; therefore, the probiotic composition should be adjusted based on the individual enterotype.

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.