Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Bioengineered human gut-on-a-chip for advancing non-clinical pharmaco-toxicology

INTRODUCTION

There is a growing need for alternative models to advance current non-clinical experimental models because they often fail to accurately predict drug responses in human clinical trials. Human organ-on-a-chip models have emerged as promising approaches for advancing the predictability of drug behaviors and responses.

AREAS COVERED

We summarize up-to-date human gut-on-a-chip models designed to demonstrate intricate interactions involving the host, microbiome, and pharmaceutical compounds since these models have been reported a decade ago. This overview covers recent advances in gut-on-a-chip models as a bridge technology between non-clinical and clinical assessments of drug toxicity and metabolism. We highlight the promising potential of gut-on-a-chip platforms, offering a reliable and valid framework for investigating reciprocal crosstalk between the host, gut microbiome, and drug compounds.

EXPERT OPINION

Gut-on-a-chip platforms can attract multiple end users as predictive, human-relevant, and non-clinical model. Notably, gut-on-a-chip platforms provide a unique opportunity to recreate a human intestinal microenvironment, including dynamic bowel movement, luminal flow, oxygen gradient, host-microbiome interactions, and disease-specific manipulations restricted in animal and in vitro cell culture models. Additionally, given the profound impact of the gut microbiome on pharmacological bioprocess, it is critical to leverage breakthroughs of gut-on-a-chip technology to address knowledge gaps and drive innovations in predictive drug toxicology and metabolism.

The metabolome-wide signature of major depressive disorder

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

Sex differences in associations of plasma metabolites with blood pressure and heart rate variability: The HELIUS study

BACKGROUND AND AIMS

Since plasma metabolites can modulate blood pressure (BP) and vary between men and women, we examined sex differences in plasma metabolite profiles associated with BP and sympathicovagal balance. Our secondary aim was to investigate associations between gut microbiota composition and plasma metabolites predictive of BP and heart rate variability (HRV).

METHODS

From the HELIUS cohort, we included 196 women and 173 men. Office systolic BP and diastolic BP were recorded, and heart rate variability (HRV) and baroreceptor sensitivity (BRS) were calculated using finger photoplethysmography. Plasma metabolomics was measured using untargeted LC-MS/MS. Gut microbiota composition was determined using 16S sequencing. We used machine learning models to predict BP and HRV from metabolite profiles, and to predict metabolite levels from gut microbiota composition.

RESULTS

In women, best predicting metabolites for systolic BP included dihomo-lineoylcarnitine, 4-hydroxyphenylacetateglutamine and vanillactate. In men, top predictors included sphingomyelins, N-formylmethionine and conjugated bile acids. Best predictors for HRV in men included phenylacetate and gentisate, which were associated with lower HRV in men but not in women. Several of these metabolites were associated with gut microbiota composition, including phenylacetate, multiple sphingomyelins and gentisate.

CONCLUSIONS

Plasma metabolite profiles are associated with BP in a sex-specific manner. Catecholamine derivatives were more important predictors for BP in women, while sphingomyelins were more important in men. Several metabolites were associated with gut microbiota composition, providing potential targets for intervention.

Circulating metabolites modulated by diet are associated with depression

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Epigenome-wide association study of incident type 2 diabetes in Black and White participants from the Atherosclerosis Risk in Communities Study

DNA methylation studies of incident type 2 diabetes in US populations are limited, and to our knowledge none included individuals of African descent living in the US. We performed an epigenome-wide association analysis of blood-based methylation levels at CpG sites with incident type 2 diabetes using Cox regression in 2,091 Black and 1,029 White individuals from the Atherosclerosis Risk in Communities study. At an epigenome-wide significance threshold of 10-7, we detected 7 novel diabetes-associated CpG sites in C1orf151 (cg05380846: HR= 0.89, p = 8.4 × 10-12), ZNF2 (cg01585592: HR= 0.88, p = 1.6 × 10-9), JPH3 (cg16696007: HR= 0.87, p = 7.8 × 10-9), GPX6 (cg02793507: HR= 0.85, p = 2.7 × 10-8 and cg00647063: HR= 1.20, p = 2.5 × 10-8), chr17q25 (cg16865890: HR= 0.8, p = 6.9 × 10-8), and chr11p15 (cg13738793: HR= 1.11, p = 7.7 × 10-8). The CpG sites at C1orf151, ZNF2, JPH3 and GPX6, were identified in Black adults, chr17q25 was identified in White adults, and chr11p15 was identified upon meta-analyzing the two groups. The CpG sites at JPH3 and GPX6 were likely associated with incident type 2 diabetes independent of BMI. All the CpG sites, except at JPH3, were likely consequences of elevated glucose at baseline. We additionally replicated known type 2 diabetes-associated CpG sites including cg19693031 at TXNIP, cg00574958 at CPT1A, cg16567056 at PLBC2, cg11024682 at SREBF1, cg08857797 at VPS25, and cg06500161 at ABCG1, 3 of which were replicated in Black adults at the epigenome-wide threshold. We observed modest increase in type 2 diabetes variance explained upon addition of the significantly associated CpG sites to a Cox model that included traditional type 2 diabetes risk factors and fasting glucose (increase from 26.2% to 30.5% in Black adults; increase from 36.9% to 39.4% in White adults). We examined if groups of proximal CpG sites were associated with incident type 2 diabetes using a gene-region specific and a gene-region agnostic differentially methylated region (DMR) analysis. Our DMR analyses revealed several clusters of significant CpG sites, including a DMR consisting of a previously discovered CpG site at ADCY7 and promoter regions of TP63 which were differentially methylated across all race groups. This study illustrates improved discovery of CpG sites/regions by leveraging both individual CpG site and DMR analyses in an unexplored population. Our findings include genes linked to diabetes in experimental studies (e.g., GPX6, JPH3, and TP63), and future gene-specific methylation studies could elucidate the link between genes, environment, and methylation in the pathogenesis of type 2 diabetes.

MultiDS-MDA: Integrating multiple data sources into heterogeneous network for predicting novel metabolite-drug associations

Metabolic processes in the human body play an important role in maintaining normal life activities, and the abnormal concentration of metabolites is closely related to the occurrence and development of diseases. The use of drugs is considered to have a major impact on metabolism, and drug metabolites can contribute to efficacy, drug toxicity and drug-drug interaction. However, our understanding of metabolite-drug associations is far from complete, and individual data source tends to be incomplete and noisy. Therefore, the integration of various types of data sources for inferring reliable metabolite-drug associations is urgently needed. In this study, we proposed a computational framework, MultiDS-MDA, for identifying metabolite-drug associations by integrating multiple data sources, including chemical structure information of metabolites and drugs, the relationships of metabolite-gene, metabolite-disease, drug-gene and drug-disease, the data of gene ontology (GO) and disease ontology (DO) and known metabolite-drug connections. The performance of MultiDS-MDA was evaluated by 5-fold cross-validation, which achieved an area under the ROC curve (AUROC) of 0.911 and an area under the precision-recall curve (AUPRC) of 0.907. Additionally, MultiDS-MDA showed outstanding performance compared with similar approaches. Case studies for three metabolites (cholesterol, thromboxane B2 and coenzyme Q10) and three drugs (simvastatin, pravastatin and morphine) also demonstrated the reliability and efficiency of MultiDS-MDA, and it is anticipated that MultiDS-MDA will serve as a powerful tool for future exploration of metabolite-drug interactions and contribute to drug development and drug combination.

Metabolomics signatures of depression: the role of symptom profiles

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an "atypical/energy-related" symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An 'AES profile' score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10^-12), isoleucine (β = 0.13, p = 1.45*10^-10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10^-9) and saturated fatty acid levels (β = 0.09, p = 3.68*10^-10), and lower high-density lipoproteins cholesterol (β = -0.07, p = 1.14*10^-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature-commonly linked to cardiometabolic disorders-associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression's detrimental impact on health.

A Microbial Community Cultured in Gradient Hydrogel for Investigating Gut Microbiome-Drug Interaction and Guiding Therapeutic Decisions

Despite the recognition that the gut microbiota acts a clinically significant role in cancer chemotherapy, both mechanistic understanding and translational research are still limited. Maximizing drug efficacy requires an in-depth understanding of how the microbiota contributes to therapeutic responses, while microbiota modulation is hindered by the complexity of the human body. To address this issue, a 3D experimental model named engineered microbiota (EM) is reported for bridging microbiota-drug interaction research and therapeutic decision-making. EM can be manipulated in vitro and faithfully recapitulate the human gut microbiota at the genus/species level while allowing co-culture with cells, organoids, and isolated tissues for testing drug responses. Examination of various clinical and experimental drugs by EM reveales that the gut microbiota affects drug efficacy through three pathways: immunological effects, bioaccumulation, and drug metabolism. Guided by discovered mechanisms, custom-tailored strategies are adopted to maximize the therapeutic efficacy of drugs on orthotopic tumor models with patient-derived gut microbiota. These strategies include immune synergy, nanoparticle encapsulation, and host-guest complex formation, respectively. Given the important role of the gut microbiota in influencing drug efficacy, EM will likely become an indispensable tool to guide drug translation and clinical decision-making.

Integrated serum pharmacochemistry and metabolomics reveal potential effective components and mechanisms of Shengjiang Xiexin decoction in the treatment of Clostridium difficile infection

Shengjiang Xiexin Decoction (SXD) is a widely recognized formula in Traditional Chinese Medicine (TCM) for treating diarrhea and is commonly used in clinical practice. Clostridium difficile infection (CDI) is a type of antibiotic-associated diarrhea with a rising incidence rate that has severe consequences for humans. Recent clinical applications have found significant efficacy in using SXD as an adjunct to CDI treatment. However, the pharmacodynamic substance basis and therapeutic mechanism of SXD remain unclear. This study aimed to systematically analyze the metabolic mechanisms and key pharmacodynamic components of SXD in CDI mice by combining non-targeted metabolomics of Chinese medicine and serum medicinal chemistry. We established a CDI mouse model to observe the therapeutic effect of SXD on CDI. We investigated the mechanism of action and active substance composition of SXD against CDI by analyzing 16S rDNA gut microbiota, untargeted serum metabolomics, and serum pharmacochemistry. We also constructed a multi-scale, multifactorial network for overall visualization and analysis. Our results showed that SXD significantly reduced fecal toxin levels and attenuated colonic injury in CDI model mice. Additionally, SXD partially restored CDI-induced gut microbiota composition. Non-targeted serum metabolomics studies showed that SXD not only regulated Taurine and hypotaurine metabolism but also metabolic energy and amino acid pathways such as Ascorbate and aldarate metabolism, Glycerolipid metabolism, Pentose and glucuronate interconversions, as well as body and other metabolite production in the host. Through the implementation of network analysis methodologies, we have discerned that Panaxadiol, Methoxylutcolin, Ginsenoside-Rf, Suffruticoside A, and 10 other components serve as critical potential pharmacodynamic substance bases of SXD for CDI. This study reveals the metabolic mechanism and active substance components of SXD for the treatment of CDI mice using phenotypic information, gut microbiome, herbal metabolomics, and serum pharmacochemistry. It provides a theoretical basis for SXD quality control studies.

Gut Microbiota Modulation for Therapeutic Management of Various Diseases: A New Perspective Using Stem Cell Therapy

Dysbiosis has been linked to various diseases ranging from cardiovascular, neurologic, gastrointestinal, respiratory, and metabolic illnesses to cancer. Restoring of gut microbiota balance represents an outstanding clinical target for the management of various multidrug-resistant diseases. Preservation of gut microbial diversity and composition could also improve stem cell therapy which now has diverse clinical applications in the field of regenerative medicine. Gut microbiota modulation and stem cell therapy may be considered a highly promising field that could add up towards the improvement of different diseases, increasing the outcome and efficacy of each other through mutual interplay or interaction between both therapies. Importantly, more investigations are required to reveal the cross-talk between microbiota modulation and stem cell therapy to pave the way for the development of new therapies with enhanced therapeutic outcomes. This review provides an overview of dysbiosis in various diseases and their management. It also discusses microbiota modulation via antibiotics, probiotics, prebiotics, and fecal microbiota transplant to introduce the concept of dysbiosis correction for the management of various diseases. Furthermore, we demonstrate the beneficial interactions between microbiota modulation and stem cell therapy as a way for the development of new therapies in addition to limitations and future challenges regarding the applications of these therapies.

Linking research of biomedical datasets

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

LC-MS/MS Insight into Vitamin C Restoration to Metabolic Disorder Evoked by Amyloid β in Caenorhabditis elegans CL2006

The transitional expression and aggregation of amyloid β (Aβ) are the most important causative factors leading to the deterioration of Alzheimer’s disease (AD), a commonly occurring metabolic disease among older people. Antioxidant agents such as vitamin C (Vc) have shown potential effects against AD and aging. We applied an liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method and differential metabolites strategy to explore the metabolic disorders and Vc restoration in a human Aβ transgenic (Punc-54::Aβ1–42) nematode model CL2006. We combined the LC-MS/MS investigation with the KEGG and HMDB databases and the CFM-ID machine-learning model to identify and qualify the metabolites with important physiological roles. The differential metabolites responding to Aβ activation and Vc treatment were filtered out and submitted to enrichment analysis. The enrichment showed that Aβ mainly caused abnormal biosynthesis and metabolism pathways of phenylalanine, tyrosine and tryptophan biosynthesis, as well as arginine and proline metabolism. Vc reversed the abnormally changed metabolites tryptophan, anthranilate, indole and indole-3-acetaldehyde. Vc restoration affected the tryptophan metabolism and the biosynthesis of phenylalanine, tyrosine and tryptophan. Our findings provide supporting evidence for understanding the metabolic abnormalities in neurodegenerative diseases and the repairing effect of drug interventions.

Microbiome as an immune regulator in health, disease, and therapeutics

New discoveries in drugs and drug delivery systems are focused on identifying and delivering a pharmacologically effective agent, potentially targeting a specific molecular component. However, current drug discovery and therapeutic delivery approaches do not necessarily exploit the complex regulatory network of an indispensable microbiota that has been engineered through evolutionary processes in humans or has been altered by environmental exposure or diseases. The human microbiome, in all its complexity, plays an integral role in the maintenance of host functions such as metabolism and immunity. However, dysregulation in this intricate ecosystem has been linked with a variety of diseases, ranging from inflammatory bowel disease to cancer. Therapeutics and bacteria have an undeniable effect on each other and understanding the interplay between microbes and drugs could lead to new therapies, or to changes in how existing drugs are delivered. In addition, targeting the human microbiome using engineered therapeutics has the potential to address global health challenges. Here, we present the challenges and cutting-edge developments in microbiome-immune cell interactions and outline novel targeting strategies to advance drug discovery and therapeutics, which are defining a new era of personalized and precision medicine.

Gut Microbes and Neuropathology: Is There a Causal Nexus?

The gut microbiota is a virtual organ which produces a myriad of molecules that the brain and other organs require. Humans and microbes are in a symbiotic relationship, we feed the microbes, and in turn, they provide us with essential molecules. Bacteroidetes and Firmicutes phyla account for around 80% of the total human gut microbiota, and approximately 1000 species of bacteria have been identified in the human gut. In adults, the main factors influencing microbiota structure are diet, exercise, stress, disease and medications. In this narrative review, we explore the involvement of the gut microbiota in Parkinson’s disease, Alzheimer’s disease, multiple sclerosis and autism, as these are such high-prevalence disorders. We focus on preclinical studies that increase the understanding of disease pathophysiology. We examine the potential for targeting the gut microbiota in the development of novel therapies and the limitations of the currently published clinical studies. We conclude that while the field shows enormous promise, further large-scale studies are required if a causal link between these disorders and gut microbes is to be definitively established.

Blood metabolites predicting mild cognitive impairment in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL)

Introduction

Blood metabolomics‐based biomarkers may be useful to predict measures of neurocognitive aging.

Methods

We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual‐metabolite analysis.

Results

We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as associated with neurocognitive aging outcomes. The MCI MRS comprised 61 metabolites and improved prediction accuracy from 84% (minimally adjusted model) to 89% in the entire dataset and from 75% to 87% among apolipoprotein E ε4 carriers.

Discussion

Blood metabolites may serve as biomarkers identifying individuals at risk for MCI among US Hispanics/Latinos.

The Exposome and Toxicology: A Win-Win Collaboration

The development of the exposome concept has been one of the hallmarks of environmental and health research for the last decade. The exposome encompasses the life course environmental exposures including lifestyle factors from the prenatal period onwards. It has inspired many research programs and is expected to influence environmental and health research, practices, and policies. Yet, the links bridging toxicology and the exposome concept have not been well developed. In this review, we describe how the exposome framework can interface with and influence the field of toxicology, as well as how the field of toxicology can help advance the exposome field by providing the needed mechanistic understanding of the exposome impacts on health. Indeed, exposome-informed toxicology is expected to emphasize several orientations including (1) developing approaches integrating multiple stressors, in particular chemical mixtures, as well as the interaction of chemicals with other stressors, (2) using mechanistic frameworks such as the adverse outcome pathways to link the different stressors with toxicity outcomes, (3) characterizing the mechanistic basis of long-term effects by distinguishing different patterns of exposures and further exploring the environment-DNA interface through genetic and epigenetic studies, and (4) improving the links between environmental and human health, in particular through a stronger connection between alterations in our ecosystems and human toxicology. The exposome concept provides the linkage between the complex environment and contemporary mechanistic toxicology. What toxicology can bring to exposome characterization is a needed framework for mechanistic understanding and regulatory outcomes in risk assessment.

Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

A multi-omics study of circulating phospholipid markers of blood pressure

High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.

Personalizing treatments for patients based on cardiovascular phenotyping

Introduction

Cardiovascular disease persists as the leading cause of death worldwide despite continued advances in diagnostics and therapeutics. Our current approach to patients with cardiovascular disease is rooted in reductionism, which presupposes that all patients share a similar phenotype and will respond the same to therapy; however, this is unlikely as cardiovascular diseases exhibit complex heterogeneous phenotypes.

Areas covered

With the advent of high-throughput platforms for omics testing, phenotyping cardiovascular diseases has advanced to incorporate large-scale molecular data with classical history, physical examination, and laboratory results. Findings from genomics, proteomics, and metabolomics profiling have been used to define more precise cardiovascular phenotypes and predict adverse outcomes in population-based and disease-specific patient cohorts. These molecular data have also been utilized to inform drug efficacy based on a patient's unique phenotype.

Expert opinion

Multiscale phenotyping of cardiovascular disease has revealed diversity among patients that can be used to personalize pharmacotherapies and predict outcomes. Nonetheless, precision phenotyping for cardiovascular disease remains a nascent field that has not yet translated into widespread clinical practice despite its many potential advantages for patient care. Future endeavors that demonstrate improved pharmacotherapeutic responses and associated reduction in adverse events will facilitate mainstream adoption of precision cardiovascular phenotyping.

1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

Background

Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies.

Methods

To this end, we have employed ∼26,000 ¹H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC_{5-Fold CV} = 0·94) and lipid medication usage (AUC_{5-Fold CV} = 0·90).

Findings

Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants.

Interpretation

To conclude, we provide ¹H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved.

Funding

BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Analysis of gut microbiota in patients with epilepsy treated with valproate: Results from a three months observational prospective cohort study

BACKGROUND

Growing evidence implicates the potential effect of microbiota on the pathogenesis and course of epilepsy. However, the effects of valproate (VPA), a broad spectrum anti-epileptic drugs, on gut microbiota have not been investigated in humans. This study aimed to analyze fecal microbiota in patients with epilepsy treated with valproate.

METHODS

A total of 10 participants, who were newly diagnosed of cryptogenic epilepsy with treatment naïve and received 1000 mg daily doses of VPA, were recruited in our prospective study. Microbiota compositions were evaluated at baseline and after three months of VPA treatment using 16S rDNA sequencing.

RESULTS

VPA treatment was associated with clinical improvements in all patients, but not changes in gut microbiota richness and complexity (Shannon: p = 0.82). Microbiome composition structure differences also revealed no statistical difference in dissimilarity (Adonis: p = 0.90). No statistical difference taxa were found between two groups. However, the ratio of phyla Firmicutes to Bacteriodetes (ANOVA: p = 0.037) markedly raised after three months of VPA-treatment. A correlation matrix based on the spearman correlation distance confirmed associations between specific fecal taxa and VPA-related clinical metabolic parameters, including drug concentration in the blood, total cholesterol, triglyceride, lactate dehydrogenase, alanine aminotransferase, aspartate aminotransferase and weight gain. (p < 0.05) CONCLUSIONS: Among those patients treated with VPA, characterization of the gut microbiota altered, and gut microbiota associated with weight gain and clinical biochemical indexes, suggesting that microbiome composition data might involve in the mechanisms of VPA induced metabolic disorder.

Anxiety symptom remission is associated with genetic variation of PTPRZ1 among patients with major depressive disorder treated with escitalopram

OBJECTIVES

Genome-wide analyses of antidepressant response have suggested that genes initially associated with risk for schizophrenia may also serve as promising candidates for selective serotonin reuptake inhibitor (SSRI) efficacy. Protein tyrosine phosphatase, receptor-type, zeta-1 (PTPRZ1) has previously been shown to be associated with schizophrenia, but it has not been investigated as a predictor of antidepressant efficacy. The main objective of the study was to assess whether SSRI-mediated depressive and anxiety symptom remission in Chinese patients with major depressive disorder (MDD) are associated with specific PTPRZ1 variants.

METHODS

Two independent cohorts were investigated, the first sample (N = 344) received an SSRI (i.e. fluoxetine, sertraline, citalopram, escitalopram, fluvoxamine, or paroxetine) for 8 weeks. The second sample (N = 160) only received escitalopram for 8 weeks. Hamilton Depression and Hamilton Anxiety Rating Scale scores at 8-weeks post-baseline in both cohorts were used to determine remission status. Five PTPRZ1 variants (rs12154537, rs6466810, rs6466808, rs6955395, and rs1918031) were genotyped in both cohorts.

RESULTS

Anxiety symptom remission was robustly associated with PTPRZ1 rs12154537 (P = 0.004) and the G-G-G-G haplotype (rs12154537-rs6466810-rs6466808-rs6955395; P = 0.005) in cohort 2 but not cohort 1 (mixed SSRI use). Associations with depressive symptom remission did not survive correction for multiple testing.

CONCLUSIONS

These findings suggest that PTPRZ1 variants may serve as a marker of escitalopram-mediated anxiety symptom remission in MDD.

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.

An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization

Metabolites are closely related to human disease. The interaction between metabolites and drugs has drawn increasing attention in the field of pharmacomicrobiomics. However, only a small portion of the drug-metabolite interactions were experimentally observed due to the fact that experimental validation is labor-intensive, costly, and time-consuming. Although a few computational approaches have been proposed to predict latent associations for various bipartite networks, such as miRNA-disease, drug-target interaction networks, and so on, to our best knowledge the associations between drugs and metabolites have not been reported on a large scale. In this study, we propose a novel algorithm, namely inductive logistic matrix factorization (ILMF) to predict the latent associations between drugs and metabolites. Specifically, the proposed ILMF integrates drug-drug interaction, metabolite-metabolite interaction, and drug-metabolite interaction into this framework, to model the probability that a drug would interact with a metabolite. Moreover, we exploit inductive matrix completion to guide the learning of projection matrices U and V that depend on the low-dimensional feature representation matrices of drugs and metabolites: Fm and Fd . These two matrices can be obtained by fusing multiple data sources. Thus, Fd U and Fm V can be viewed as drug-specific and metabolite-specific latent representations, different from classical LMF. Furthermore, we utilize the Vicus spectral matrix that reveals the refined local geometrical structure inherent in the original data to encode the relationships between drugs and metabolites. Extensive experiments are conducted on a manually curated "DrugMetaboliteAtlas" dataset. The experimental results show that ILMF can achieve competitive performance compared with other state-of-the-art approaches, which demonstrates its effectiveness in predicting potential drug-metabolite associations.

Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite-disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver ( https://omicscience.org/apps/mwasdisease/ ) to facilitate future research and meta-analyses.

Dual and mutual interaction between microbiota and viral infections: a possible treat for COVID-19

All of humans and other mammalian species are colonized by some types of microorganisms such as bacteria, archaea, unicellular eukaryotes like fungi and protozoa, multicellular eukaryotes like helminths, and viruses, which in whole are called microbiota. These microorganisms have multiple different types of interaction with each other. A plethora of evidence suggests that they can regulate immune and digestive systems and also play roles in various diseases, such as mental, cardiovascular, metabolic and some skin diseases. In addition, they take-part in some current health problems like diabetes mellitus, obesity, cancers and infections. Viral infection is one of the most common and problematic health care issues, particularly in recent years that pandemics like SARS and COVID-19 caused a lot of financial and physical damage to the world. There are plenty of articles investigating the interaction between microbiota and infectious diseases. We focused on stimulatory to suppressive effects of microbiota on viral infections, hoping to find a solution to overcome this current pandemic. Then we reviewed mechanistically the effects of both microbiota and probiotics on most of the viruses. But unlike previous studies which concentrated on intestinal microbiota and infection, our focus is on respiratory system's microbiota and respiratory viral infection, bearing in mind that respiratory system is a proper entry site and residence for viruses, and whereby infection, can lead to asymptomatic, mild, self-limiting, severe or even fatal infection. Finally, we overgeneralize the effects of microbiota on COVID-19 infection. In addition, we reviewed the articles about effects of the microbiota on coronaviruses and suggest some new therapeutic measures.

Leptin alters energy intake and fat mass but not energy expenditure in lean subjects

Based on studies in mice, leptin was expected to decrease body weight in obese individuals. However, the majority of the obese are hyperleptinemic and do not respond to leptin treatment, suggesting the presence of leptin tolerance and questioning the role of leptin as regulator of energy balance in humans. We thus performed detailed novel measurements and analyses of samples and data from our clinical trials biobank to investigate leptin effects on mechanisms of weight regulation in lean normo- and mildly hypo-leptinemic individuals without genetic disorders. We demonstrate that short-term leptin administration alters food intake during refeeding after fasting, whereas long-term leptin treatment reduces fat mass and body weight, and transiently alters circulating free fatty acids in lean mildly hypoleptinemic individuals. Leptin levels before treatment initiation and leptin dose do not predict the observed weight loss in lean individuals suggesting a saturable effect of leptin. In contrast to data from animal studies, leptin treatment does not affect energy expenditure, lipid utilization, SNS activity, heart rate, blood pressure or lean body mass.

Leptin treatment is effective to reduce body weight in animal models, but patients with obesity and associated hyperleptinemia do not respond well to leptin therapy. Here the authors report a retrospective analysis of four clinical trials in normo- and mildly hypoleptinemic individuals and show that leptin therapy alters food intake in the short term and reduces weight and fat mass in the long term without effects on energy expenditure.

Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions

Since the discovery of the potential role for the gut microbiota in health and disease, many studies have gone on to report its impact in various pathologies. These studies have fuelled interest in the microbiome as a potential new target for treating disease Here, we reviewed the key metabolic diseases, obesity, type 2 diabetes and atherosclerosis and the role of the microbiome in their pathogenesis. In particular, we will discuss disease associated microbial dysbiosis; the shift in the microbiome caused by medical interventions and the altered metabolite levels between diseases and interventions. The microbial dysbiosis seen was compared between diseases including Crohn’s disease and ulcerative colitis, non-alcoholic fatty liver disease, liver cirrhosis and neurodegenerative diseases, Alzheimer’s and Parkinson’s. This review highlights the commonalities and differences in dysbiosis of the gut between diseases, along with metabolite levels in metabolic disease vs. the levels reported after an intervention. We identify the need for further analysis using systems biology approaches and discuss the potential need for treatments to consider their impact on the microbiome.

Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term 'metabolomics' refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

CONTEXT

There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease.

OBJECTIVE

To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D).

DESIGN

3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts.

MAIN OUTCOME MEASURES

Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing.

RESULTS

In total, 125 metabolites were significantly associated (PFDR = 1.5×10-32 - 0.046; β = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after.

CONCLUSIONS

This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation.