Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease

HDL cholesterol esters mediate the genetic link between sedentary behavior and urological cancers: Insights from mediation and validation analyses

This study explores the causal relationship between sedentary behavior and urological cancers, focusing on bladder cancer (BC), prostate cancer, and kidney cancer, using Bayesian Mendelian randomization and mediation analysis. A two-sample Mendelian randomization (MR) framework was employed, using genetic variants as instrumental variables. Bayesian and multivariate MR assessed causal effects of sedentary behaviors (TV watching, computer use, driving) on urological cancers. Sensitivity analyses (MR-Egger, MR-PRESSO, and Cochran Q) ensured robustness. Mediation analysis identified high-density lipoprotein (HDL) cholesterol ester levels as a primary mediator, validated through meta-analysis. Prolonged TV watching was significantly associated with increased BC risk (OR = 2.908; 95% CI = 1.221-6.930; P = .015). Mediation analysis showed small HDL cholesterol ester levels mediated 17.5% of this effect. No causal relationships were observed between computer use or driving and the cancers. Sensitivity analyses confirmed robust findings without heterogeneity or pleiotropy. Prolonged TV watching increases BC risk, mediated by small HDL cholesterol ester levels. Sedentary behavior is a modifiable risk factor, highlighting the importance of lifestyle interventions in prevention.

Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors

Motivation

The increasing use of big data and optimized prediction methods in metabolomics requires techniques aligned with biological assumptions to improve early symptom diagnosis. One major challenge in predictive data analysis is handling confounding factors-variables influencing predictions but not directly included in the analysis.

Results

Detecting and correcting confounding factors enhances prediction accuracy, reducing false negatives that contribute to diagnostic errors. This study reviews concept drift detection methods in metabolomic predictions and selects the most appropriate ones. We introduce a new implementation of concept drift analysis in predictive classifiers using metabolomics data. Known confounding factors were confirmed, validating our approach and aligning it with conventional methods. Additionally, we identified potential confounding factors that may influence biomarker analysis, which could introduce bias and impact model performance.

Availability and implementation

Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.

Mechanisms of sepsis-induced acute liver injury: a comprehensive review

Sepsis is a severe, often life-threatening form of organ dysfunction that arises from an inappropriately regulated host response to infectious pathogen exposure. As the largest gland in the body, the liver serves as a regulatory hub for metabolic, immune, and detoxification activity. It is also an early sepsis target organ such that hepatic dysfunction is observed in 34-46% of patients with sepsis. The precise mechanisms that give rise to sepsis-induced liver injury, however, remain incompletely understood. Based on the research conducted to date, dysregulated systemic inflammation, microbial translocation, microcirculatory abnormalities, cell death, metabolic dysfunction, and liver inflammation may all contribute to the liver damage that can arise in the context of septicemia. This review was developed to provide an overview summarizing the potential mechanisms underlying sepsis-induced liver injury, informing the selection of potential targets for therapeutic intervention and providing a framework for the alleviation of patient symptoms and the improvement of prognostic outcomes.

Investigating the causal relationship between the plasma lipidome and cholangiocarcinoma mediated by immune cells: a mediation Mendelian randomization study

The plasma lipidome and immune cells are instrumental in shaping the health profile of an organism, and their influence on diseases is profound. However, the intricate interactions between cholangiocarcinoma (CCA) and these physiological components have yet to be comprehensively explored. Employing Mendelian randomization (MR), our study delved into the causal links among immune cells, the lipidome, and CCA. The research design meticulously considered both the direct associations and the mediating roles of immune cells within the complex interplay between the lipidome and CCA. Our analysis uncovered significant correlations between the levels of Sphingomyelin (d34:1), Phosphatidylcholine (0-16:0, 22:5) and Sterol ester (27:1/16:0) and CCA. Moreover, we have pinpointed various immune cells that play a mediating role in the impact of the lipidome on CCA. For example, Sphingomyelin (d34:1) can impact CCA through the IgD on IgD+ CD38- unswitched memory (unsw mem) B cell (B cell panel), IgD on unsw mem (B cell panel) and Naive CD4+ %CD4+ (maturation stages of T cell). The proportion of mediating effects further sheds light on the intricate interplay among the lipidome, immune cells, and their cumulative influence on CCA. Our study illuminates the intricate relationship among the lipidome, immune cells, and CCA. These findings suggest that the lipidome could serve as a promising and potentially effective therapeutic target in the treatment of CCA.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Multi-omics integration and immune profiling identify possible causal networks leading to uterine microbiome dysbiosis in dairy cows that develop metritis

BACKGROUND

Cows that develop metritis experience dysbiosis of their uterine microbiome, where opportunistic pathogens overtake uterine commensals. An effective immune response is critical for maintaining uterine health. Nonetheless, periparturient cows experience immune dysregulation, which seems to be intensified by prepartum over-condition. Herein, Bayesian networks were applied to investigate the directional correlations between prepartum body weight (BW), BW loss, pre- and postpartum systemic immune profiling and plasma metabolome, and postpartum uterine metabolome and microbiome.

RESULTS

The Bayesian network analysis showed a positive directional correlation between prepartum BW, prepartum BW loss, and plasma fatty acids at parturition, suggesting that heavier cows were in lower energy balance than lighter cows. There was a positive directional correlation between prepartum BW, prepartum systemic leukocyte death, immune activation, systemic inflammation, and metabolomic changes associated with oxidative stress prepartum and at parturition. Immune activation and systemic inflammation were characterized by increased proportion of circulating polymorphonuclear cells (PMN) prepartum, B-cell activation at parturition, interleukin-8 prepartum and at parturition, and interleukin-1β at parturition. These immune changes together with plasma fatty acids at parturition had a positive directional correlation with PMN extravasation postpartum, which had a positive directional correlation with uterine metabolites associated with tissue damage. These results suggest that excessive PMN migration to the uterus leads to excessive endometrial damage. The aforementioned changes had a positive directional correlation with Fusobacterium, Porphyromonas, and Bacteroides in cows that developed metritis, suggesting that excessive tissue damage may disrupt physical barriers or increase substrate availability for bacterial growth.

CONCLUSIONS

This work provides robust mechanistic hypotheses for how prepartum BW may impact peripartum immune and metabolic profiles, which may lead to uterine opportunistic pathogens overgrowth and metritis development.

A genome-wide association analysis reveals new pathogenic pathways in gout

Gout is a chronic disease that is caused by an innate immune response to deposited monosodium urate crystals in the setting of hyperuricemia. Here, we provide insights into the molecular mechanism of the poorly understood inflammatory component of gout from a genome-wide association study (GWAS) of 2.6 million people, including 120,295 people with prevalent gout. We detected 377 loci and 410 genetically independent signals (149 previously unreported loci in urate and gout). An additional 65 loci with signals in urate (from a GWAS of 630,117 individuals) but not gout were identified. A prioritization scheme identified candidate genes in the inflammatory process of gout, including genes involved in epigenetic remodeling, cell osmolarity and regulation of NOD-like receptor protein 3 (NLRP3) inflammasome activity. Mendelian randomization analysis provided evidence for a causal role of clonal hematopoiesis of indeterminate potential in gout. Our study identifies candidate genes and molecular processes in the inflammatory pathogenesis of gout suitable for follow-up studies.

Facing the challenges to shorten the diagnostic odyssey: first Whole Genome Sequencing experience of a Colombian cohort with suspected rare diseases

Exome and genome sequencing (ES/GS) are routinely used for the diagnosis of genetic diseases in developed countries. However, their implementation is limited in countries from Latin America. We aimed to describe the results of GS in patients with suspected rare genetic diseases in Colombia. We studied 501 patients from 22 healthcare sites from January to December 2022. GS was performed in the index cases using dried blood spots on filtercards. Ancestry analysis was performed under iAdmix. Multiomic testing was performed when needed (biomarker, enzymatic activity, RNA-seq). All tests were performed at an accredited genetic laboratory. Ethnicity prediction data confirmed that 401 patients (80%) were mainly of Amerindian origin. A genetic diagnosis was established for 142 patients with a 28.3% diagnostic yield. The highest diagnostic yield was achieved for pathologies with a metabolic component and syndromic disorders (p < 0.001). Young children had a median of 1 year of diagnostic odyssey, while the median time for adults was significantly longer (15 years). Patients with genetic syndromes have spent more than 75% of their life without a diagnosis, while for patients with neurologic and neuromuscular diseases, the time of the diagnostic odyssey tended to decrease with age. Previous testing, specifically karyotyping or chromosomal microarray were significantly associated with a longer time to reach a definitive diagnosis (p < 0.01). Furthermore, one out of five patients that had an ES before could be diagnosed by GS. The Colombian genome project is the first Latin American study reporting the experience of systematic use of diagnostic GS in rare diseases.

Systemic dysregulation and molecular insights into poor influenza vaccine response in the aging population

Vaccination-induced protection against influenza is greatly diminished and increasingly heterogeneous with age. We investigated longitudinally (up to five time points) a cohort of 234 vaccinated >65-year-old vaccinees with adjuvanted vaccine FluAd across two independent seasons. System-level analyses of multiomics datasets measuring six modalities and serological data revealed that poor responders lacked time-dependent changes in response to vaccination as observed in responders, suggestive of systemic dysregulation in poor responders. Multiomics integration revealed key molecules and their likely role in vaccination response. High prevaccination plasma interleukin-15 (IL-15) concentrations negatively associated with antibody production, further supported by experimental validation in mice revealing an IL-15-driven natural killer cell axis explaining the suppressive role in vaccine-induced antibody production as observed in poor responders. We propose a subset of long-chain fatty acids as modulators of persistent inflammation in poor responders. Our findings provide a potential link between low-grade chronic inflammation and poor vaccination response and open avenues for possible pharmacological interventions to enhance vaccine responses.

Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease

Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.

The RESIST Senior Individuals Cohort: Design, participant characteristics and aims

The number of older adults worldwide is growing exponentially. However, while living longer, older individuals are more susceptible to both non-infectious and infectious diseases, at least in part due to alterations of the immune system. Here, we report on a prospective cohort study investigating the influence of age on immune responses and susceptibility to infection. The RESIST Senior Individuals (SI) cohort was established as a general population cohort with a focus on the elderly, enrolling an age- and sex-stratified sample of 650 individuals (n = 100 20-39y, n = 550 61-94y, 2019-2023, Hannover, Germany). It includes clinical, demographic, and lifestyle data and also extensive biomaterial sampling. Initial insights indicate that the SI cohort exhibits characteristics of the aging immune system and the associated susceptibility to infection, thereby providing a suitable platform for the decoding of age-related alterations of the immune system and unraveling the molecular mechanisms underlying the impaired immune responsiveness in aging populations by exploring comprehensive, unbiased multi-omics datasets.

Alanine, a potential amino acid biomarker of pediatric sepsis: a pilot study in PICU

Sepsis is characterized by a metabolic disorder of amino acid occurs in the early stage; however, the profile of serum amino acids and their alterations associated with the onset of sepsis remain unclear. Thus, our objective is to identify the specific kinds of amino acids as diagnostic biomarkers in pediatric patients with sepsis. Serum samples were collected from patients with sepsis admitted to the pediatric intensive care unit (PICU) between January 2019 and December 2019 on the 1st, 3rd and 7th day following admission. Demographic and laboratory variables were also retrieved from the medical records specified times. Serum amino acid concentrations were detected by UPLC-MS/MS system. PLS-DA (VIP > 1.0) and Kruskal-Wallis test (p < 0.05) were employed to identify potential biomarkers. Spearman's rank correlation analysis was conducted to find the potential association between amino acid levels and clinical features. The diagnostic utility for pediatric sepsis was assessed using receiver operating characteristic (ROC) curve analysis. Most of amino acid contents in serum were significantly decreased in patients with sepsis, but approached normal levels by the seventh day post-diagnosis. Threonine (THR), lysine (LYS), valine (VAL) and alanine (ALA) emerged as potential biomarkers related for sepsis occurrence, though they were not associated with PELOD/PELOD-2 scores. Moreover, alterations in serum THR, LYS and ALA were linked to complications of brain injury, and serum ALA levels were also related to sepsis-associated acute kidney injury. Further analysis revealed that ALA was significantly correlated with the Glasgow score, serum lactate and glucose levels, C-reactive protein (CRP), and other indicators for liver or kidney dysfunction. Notably, the area under the ROC curve (AUC) for ALA in distinguishing sepsis from healthy controls was 0.977 (95% CI: 0.925-1.000). The serum amino acid profile of children with sepsis is significantly altered compared to that of healthy controls. Notably, ALA shows promise as a potential biomarker for the early diagnosis in septic children.

Sex-specific epigenetic signatures of circulating urate and its increase after BCG vaccination

Background

Urate concentration and the physiological regulation of urate homeostasis exhibit clear sex differences. DNA methylation has been shown to explain a substantial proportion of serum urate variance, mediate the genetic effect on urate concentration, and co-regulate with cardiometabolic traits. However, whether urate concentration is associated with DNA methylation in a sex-dependent manner is unknown. Additionally, it is worth investigating if urate changes after perturbations, such as vaccination, are associated with DNA methylation in a sex-specific manner.

Methods

We investigated the association between DNA methylation and serum urate concentrations in a Dutch cohort of 325 healthy individuals. Urate concentration and DNA methylation were measured before and after Bacillus Calmette-Guérin (BCG) vaccination, used as a perturbation associated with increased gout flares. The association analysis included united, interaction, and sex-stratified analysis. Validation of the identified CpG sites was conducted using three independent cohorts.

Results

215 CpG sites were associated with serum urate in males, while 5 CpG sites were associated with serum urate in females, indicating sex-specific associations. Circulating urate concentrations significantly increased after BCG vaccination, and baseline DNA methylation was associated with differences in urate concentration before and after vaccination in a sex-specific manner. The CpG sites associated with urate concentration in males were enriched in neuro-protection pathways, whereas in females, the urate change-associated CpG sites were related to lipid and glucose metabolism.

Conclusion

Our study enhances the understanding of how epigenetic factors contribute to regulating serum urate levels in a sex-specific manner. These insights have significant implications for the diagnosis, prevention, and treatment of various urate-related diseases and highlight the importance of personalized and sex-specific approaches in medicine.

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

Intake Biomarkers for Nutrition and Health: Review and Discussion of Methodology Issues

Metabolomics profiles from blood, urine, or other body fluids have the potential to assess intakes of foods and nutrients objectively, thereby strengthening nutritional epidemiology research. Metabolomics platforms may include targeted components that estimate the relative concentrations for individual metabolites in a predetermined set, or global components, typically involving mass spectrometry, that estimate relative concentrations more broadly. While a specific metabolite concentration usually correlates with the intake of a single food or food group, multiple metabolites may be correlated with the intake of certain foods or with specific nutrient intakes, each of which may be expressed in absolute terms or relative to total energy intake. Here, I briefly review the progress over the past 20 years on the development and application intake biomarkers for foods/food groups, nutrients, and dietary patterns, primarily by drawing from several recent reviews. In doing so, I emphasize the criteria and study designs for candidate biomarker identification, biomarker validation, and intake biomarker application. The use of intake biomarkers for diet and chronic disease association studies is still infrequent in nutritional epidemiology research. My comments here will derive primarily from our research group's recent contributions to the Women's Health Initiative cohorts. I will complete the contribution by describing some opportunities to build on the collective 20 years of effort, including opportunities related to the metabolomics profiling of blood and urine specimens from human feeding studies that approximate habitual diets.

Circulating metabolome landscape in Lynch syndrome

Circulating metabolites systemically reflect cellular processes and can modulate the tissue microenvironment in complex ways, potentially impacting cancer initiation processes. Genetic background increases cancer risk in individuals with Lynch syndrome; however, not all carriers develop cancer. Various lifestyle factors can influence Lynch syndrome cancer risk, and lifestyle choices actively shape systemic metabolism, with circulating metabolites potentially serving as the mechanical link between lifestyle and cancer risk. This study aims to characterize the circulating metabolome of Lynch syndrome carriers, shedding light on the energy metabolism status in this cancer predisposition syndrome.This study consists of a three-group cross-sectional analysis to compare the circulating metabolome of cancer-free Lynch syndrome carriers, sporadic colorectal cancer (CRC) patients, and healthy non-carrier controls. We detected elevated levels of circulating cholesterol, lipids, and lipoproteins in LS carriers. Furthermore, we unveiled that Lynch syndrome carriers and CRC patients displayed similar alterations compared to healthy non-carriers in circulating amino acid and ketone body profiles. Overall, cancer-free Lynch syndrome carriers showed a unique circulating metabolome landscape.This study provides valuable insights into the systemic metabolic landscape of Lynch syndrome individuals. The findings hint at shared metabolic patterns between cancer-free Lynch syndrome carriers and CRC patients.

Evaluating the Effects of Omega-3 Polyunsaturated Fatty Acids on Inflammatory Bowel Disease via Circulating Metabolites: A Mediation Mendelian Randomization Study

Epidemiological evidence regarding the effect of omega-3 polyunsaturated fatty acid (PUFA) supplementation on inflammatory bowel disease (IBD) is conflicting. Additionally, little evidence exists regarding the effects of specific omega-3 components on IBD risk. We applied two-sample Mendelian randomization (MR) to disentangle the effects of omega-3 PUFAs (including total omega-3, α-linolenic acid, eicosapentaenoic acid (EPA), or docosahexaenoic acid (DHA)) on the risk of IBD, Crohn's disease (CD) and ulcerative colitis (UC). Our findings indicated that genetically predicted increased EPA concentrations were associated with decreased risk of IBD (odds ratio 0.78 (95% CI 0.63-0.98)). This effect was found to be mediated through lower levels of linoleic acid and histidine metabolites. However, we found limited evidence to support the effects of total omega-3, α-linolenic acid, and DHA on the risks of IBD. In the fatty acid desaturase 2 (FADS2) region, robust colocalization evidence was observed, suggesting the primary role of the FADS2 gene in mediating the effects of omega-3 PUFAs on IBD. Therefore, the present MR study highlights EPA as the predominant active component of omega-3 fatty acids in relation to decreased risk of IBD, potentially via its interaction with linoleic acid and histidine metabolites. Additionally, the FADS2 gene likely mediates the effects of omega-3 PUFAs on IBD risk.

Association of alpha-aminoadipic acid with cardiometabolic risk factors in healthy and high-risk individuals

Introduction

Plasma levels of the metabolite alpha-aminoadipic acid (2-AAA) have been associated with risk of type 2 diabetes (T2D) and atherosclerosis. However, little is known about the relationship of 2-AAA to other cardiometabolic risk markers in pre-disease states, or in the setting of comorbid disease.

Methods

We measured circulating 2-AAA using two methods in 1) a sample of 261 healthy individuals (2-AAA Study), and 2) in a sample of 134 persons comprising 110 individuals with treated HIV, with or without T2D, a population at high risk of metabolic disease and cardiovascular events despite suppression of circulating virus, and 24 individuals with T2D without HIV (HATIM Study). We examined associations between plasma 2-AAA and markers of cardiometabolic health within each cohort.

Results and discussion

We observed differences in 2-AAA by sex and race in both cohorts, with higher levels observed in men compared with women, and in Asian compared with Black or white individuals (P<0.05). There was no significant difference in 2-AAA by HIV status within individuals with T2D in the HATIM Study. We confirmed associations between 2-AAA and dyslipidemia in both cohorts, where high 2-AAA associated with low HDL cholesterol (P<0.001) and high triglycerides (P<0.05). As expected, within the cohort of people with HIV, 2-AAA was higher in the setting of T2D compared to pre-diabetes or normoglycemia (P<0.001). 2-AAA was positively associated with body mass index (BMI) in the 2-AAA Study, and with waist circumference and measures of visceral fat volume in HATIM (all P<0.05). Further, 2-AAA associated with increased liver fat in persons with HIV (P<0.001). Our study confirms 2-AAA as a marker of cardiometabolic risk in both healthy individuals and those at high cardiometabolic risk, reveals relationships with adiposity and hepatic steatosis, and highlights important differences by sex and race. Further studies are warranted to establish molecular mechanisms linking 2-AAA to disease in other high-risk populations.

mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.

Association of alpha-aminoadipic acid (2-AAA) with cardiometabolic risk factors in healthy and high-risk individuals

Plasma levels of the metabolite alpha-aminoadipic acid (2-AAA) have been associated with risk of type 2 diabetes (T2D) and atherosclerosis. However, little is known about the relationship of 2-AAA to other cardiometabolic risk markers in pre-disease states, or in the setting of comorbid disease. We measured circulating 2-AAA using two methods in 1) a sample of 261 healthy individuals (2-AAA Study), and 2) in a sample of 134 persons comprising 110 individuals with treated HIV, with or without T2D, a population at high risk of metabolic disease and cardiovascular events despite suppression of circulating virus, and 24 individuals with T2D without HIV (HATIM Study). We examined associations between plasma 2-AAA and markers of cardiometabolic health within each cohort. We observed differences in 2-AAA by sex and race in both cohorts, with higher levels observed in men compared with women, and in Asian compared with Black or white individuals (P<0.05). There was no significant difference in 2-AAA by HIV status within individuals with T2D in the HATIM Study. We confirmed associations between 2-AAA and dyslipidemia in both cohorts where high 2-AAA associated with low HDL cholesterol (P<0.001) and high triglycerides (P<0.05). As expected, within the cohort of people with HIV, 2-AAA was higher in the setting of T2D compared to pre-diabetes or normoglycemia (P<0.001). 2-AAA was positively associated with body mass index (BMI) in the 2-AAA Study, and with waist circumference and measures of visceral fat volume in HATIM (all P<0.05). Further, 2-AAA associated with increased liver fat in persons with HIV (P<0.001). Our study confirms 2-AAA as a marker of cardiometabolic risk in both healthy individuals and those at high cardiometabolic risk, reveals relationships with adiposity and hepatic steatosis, and highlights important differences by sex and race. Further studies are warranted to establish molecular mechanisms linking 2-AAA to disease in other high-risk populations.

The Relationship of Omega-3 Fatty Acids with Dementia and Cognitive Decline: Evidence from Prospective Cohort Studies of Supplementation, Dietary Intake, and Blood Markers

Previous data have linked omega-3 fatty acids with risk of dementia. We aimed to assess the longitudinal relationships of omega-3 polyunsaturated fatty acid intake as well as blood biomarkers with risk of Alzheimer's disease (AD), dementia, or cognitive decline. Longitudinal data were derived from 1135 participants without dementia (mean age = 73 y) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to evaluate the associations of omega-3 fatty acid supplementation and blood biomarkers with incident AD during the 6-y follow-up. A meta-analysis of published cohort studies was further conducted to test the longitudinal relationships of dietary intake of omega-3 and its peripheral markers with all-cause dementia or cognitive decline. Causal dose-response analyses were conducted using the robust error meta-regression model. In the ADNI cohort, long-term users of omega-3 fatty acid supplements exhibited a 64% reduced risk of AD (hazard ratio: 0.36, 95% confidence interval: 0.18, 0.72; P = 0.004). After incorporating 48 longitudinal studies involving 103,651 participants, a moderate-to-high level of evidence suggested that dietary intake of omega-3 fatty acids could lower risk of all-cause dementia or cognitive decline by ∼20%, especially for docosahexaenoic acid (DHA) intake (relative risk [RR]: 0.82, I2 = 63.6%, P = 0.001) and for studies that were adjusted for apolipoprotein APOE ε4 status (RR: 0.83, I2 = 65%, P = 0.006). Each increment of 0.1 g/d of DHA or eicosapentaenoic acid (EPA) intake was associated with an 8% ∼ 9.9% (Plinear < 0.0005) lower risk of cognitive decline. Moderate-to-high levels of evidence indicated that elevated levels of plasma EPA (RR: 0.88, I2 = 38.1%) and erythrocyte membrane DHA (RR: 0.94, I2 = 0.4%) were associated with a lower risk of cognitive decline. Dietary intake or long-term supplementation of omega-3 fatty acids may help reduce risk of AD or cognitive decline.

Ultraviolet exposure regulates skin metabolome based on the microbiome

Skin metabolites (< 1500 Da) play a critical role in barrier function, hydration, immune response, microbial invasion, and allergen penetration. We aimed to understand the global metabolic profile changes of the skin in relation to the microbiome and UV exposure and exposed germ-free (devoid of microbiome), disinfected mice (partially devoid of skin microbiome) and control mice with intact microbiome to immunosuppressive doses of UVB radiation. Targeted and untargeted lipidome and metabolome profiling was performed with skin tissue by high-resolution mass spectrometry. UV differentially regulated various metabolites such as alanine, choline, glycine, glutamine, and histidine in germ-free mice compared to control mice. Membrane lipid species such as phosphatidylcholine, phosphatidylethanolamine, and sphingomyelin were also affected by UV in a microbiome-dependent manner. These results shed light on the dynamics and interactions between the skin metabolome, microbiome, and UV exposure and open new avenues for the development of metabolite- or lipid-based applications to maintain skin health.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Cytokine production by newborns: influence of sex and season of birth

BACKGROUND

Immune signatures at birth could be associated with clinical outcomes and will improve our understanding of immunity prenatal programming.

METHODS

Data come from 235 newborns from the cohort study NELA. Production of cytokines was determined using Luminex technology. Associations between cytokine concentrations with sex and season of birth were examined by multivariate regression models.

RESULTS

Umbilical cord blood cells produced high levels of inflammatory cytokines, moderate levels of Th1/Th2/Tr-related cytokines, and low levels of Th17 cytokines. Compared to females, male newborn cells secreted higher levels of Th2 (peptidoglycan-stimulated IL-13, odds ratio [OR] = 2.26; 95% CI 1.18, 4.31, p value = 0.013) and Th17 (polyinosinic:polycytidylic acid-stimulated IL-23, OR = 1.82, 95% CI 1.01, 3.27, p value = 0.046) and lower levels of Th1 (olive-stimulated IL-2, OR = 0.56, 95% CI 0.31, 0.99, p value = 0.047) cytokines. Also, children born during warm seasons showed decreased innate cytokine response to peptidoglycan (IL-6, OR = 0.28, 95% CI 0.15, 0.52, p value < 0.001) compared to those born in cold seasons; meanwhile, adaptive immunity cytokines were more frequently secreted by children born during warm seasons in response to allergen extracts (IL-10, OR = 2.11, 95% CI 1.12, 3.96, p value = 0.020; IL-17F, OR = 3.31, 95% CI 1.83, 5.99, p value < 0.001).

CONCLUSION

Newborns showed specific cytokines signatures influenced by sex and season of birth.

IMPACT

There is a limited number of population-based studies on the immune status at birth and the influence of prenatal and perinatal factors on it. Characterization of cytokine signatures at birth related to the prenatal environment could improve our understanding of immunity prenatal programming. Newborns exhibit specific unstimulated and stimulated cytokine signatures influenced by sex and season of birth. Unstimulated and stimulated cytokine signatures in newborns may be associated with the development of related clinical outcomes later in life.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.

Causal analysis of serum polyunsaturated fatty acids with juvenile idiopathic arthritis and ocular comorbidity

BACKGROUND & OBJECTIVE

To investigate the causal effects of plasma Polyunsaturated fatty acids (PUFAs) on the risk of juvenile idiopathic arthritis (JIA) and ocular comorbidity through Mendelian randomization (MR) analysis.

METHODS

Genetic variants (formerly single nucleotide polymorphisms, SNPs) that are strongly associated with PUFAs levels (P < 5×10-8) were selected as instrumental variables. Summary-level MR was performed with outcome estimates for JIA (n = 31,142) and JIA associated iridocyclitis (n = 94,197). The inverse variance-weighted (IVW) method was employed as the main approach to combine the estimation for each SNP. Two set of models with summary statistics were conducted and multiple sensitivity analyses were applied for testing of pleiotropic bias.

RESULTS

In model 1, genetically predicted n-6 PUFAs linoleic acid (LA) and arachidonic acid (AA) were associated with lower and higher risk of JIA associated iridocyclitis using IVW (ORLA = 0.940, 95% CI: 0.895-0.988, P = 0.015; ORAA = 1.053, 95% CI: 1.007-1.101, P = 0.024). No such association was observed between each plasma PUFAs and JIA susceptibility (P > 0.05). In further MR analysis, results from model 2 also showed a consistent trend. Besides, multiple sensitivity analyses revealed that there was no obvious evidence for unknown pleiotropy (P > 0.05).

CONCLUSIONS

Our MR study provides genetic evidence on the possible causality that plasma LA level might protect against JIA associated iridocyclitis, whereas AA was responsible for opposite effect.

Identification of diagnostic markers related to fecal and plasma metabolism in primary Sjögren's syndrome

BACKGROUND

Accurate diagnostic techniques for patients with primary Sjögren's syndrome (pSS) are needed. This study aimed to investigate new biomarkers related to fecal and plasma metabolism from pSS patients.

METHODS

The feces and plasma of 21 pSS patients and 18 controls admitted to the Second Hospital of Shanxi Medical University were collected for analysis. Metabolites in feces and plasma were quantified using liquid chromatography-mass spectrometry. The metabolic pathway alterations caused by pSS were studied and the expression of metabolites in the intersecting pathway was analyzed in the feces and plasma of pSS patients. Metabolites that showed the same alterations in feces and plasma in pSS patients were considered as diagnostic markers and receiver operating characteristic curves were generated to analyze the sensitivity of these markers in diagnosing pSS.

RESULTS

There were 114 and 92 upregulated metabolites and 54 and 125 downregulated metabolites in the feces and plasma of pSS patients, respectively. These metabolites were enriched in 8 pathways for feces and 12 pathways for plasma. Arginine biosynthesis, Linoleic acid metabolism, Tyrosine metabolism, Taurine and hypotaurine metabolism were pathways enriched by metabolites in both samples. Twelves metabolites were enriched in the above four pathways, while only 9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE and 2-Hydroxyethanesulfonate showed the same trend. The candidate diagnostic markers were all predictive, with better diagnostic sensitivity in plasma samples.

CONCLUSIONS

9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE, 2-Hydroxyethanesulfonate were metabolism-related diagnostic markers for pSS feces and plasma.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

A guide to systems-level immunomics

The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.

Plasma metabolome predicts trained immunity responses after antituberculosis BCG vaccination

The antituberculosis vaccine Bacillus Calmette–Guérin (BCG) induces nonspecific protection against heterologous infections, at least partly through induction of innate immune memory (trained immunity). The amplitude of the response to BCG is variable, but the factors that influence this response are poorly understood. Metabolites, either released by cells or absorbed from the gut, are known to influence immune responses, but whether they impact BCG responses is not known. We vaccinated 325 healthy individuals with BCG, and collected blood before, 2 weeks and 3 months after vaccination, to assess the influence of circulating metabolites on the immune responses induced by BCG. Circulating metabolite concentrations after BCG vaccination were found to have a more pronounced impact on trained immunity responses, such as the increase in IL-1β and TNF-α production upon Staphylococcus aureus stimulation, than on specific adaptive immune memory, assessed as IFN-γ production in response to Mycobacterium tuberculosis. Circulating metabolites at baseline were able to predict trained immunity responses at 3 months after vaccination and enrichment analysis based on the metabolites positively associated with trained immunity revealed enrichment of the tricarboxylic acid (TCA) cycle and glutamine metabolism, both of which were previously found to be important for trained immunity. Several new metabolic pathways that influence trained immunity were identified, among which taurine metabolism associated with BCG-induced trained immunity, a finding validated in functional experiments. In conclusion, circulating metabolites are important factors influencing BCG-induced trained immunity in humans. Modulation of metabolic pathways may be a novel strategy to improve vaccine and trained immunity responses.

The response to the BCG vaccine, which provides protection against tuberculosis as well as unrelated pathogens, is variable. This study shows that the baseline plasma metabolome is able to partially predict trained immunity upon BCG vaccination, identifying the metabolome as a potential source of this heterogeneity.

The Genetic Risk for COVID-19 Severity Is Associated With Defective Immune Responses

Recent genome-wide association studies (GWASs) of COVID-19 patients of European ancestry have identified genetic loci significantly associated with disease severity. Here, we employed the detailed clinical, immunological and multi-omics dataset of the Human Functional Genomics Project (HFGP) to explore the physiological significance of the host genetic variants that influence susceptibility to severe COVID-19. A genomics investigation intersected with functional characterization of individuals with high genetic risk for severe COVID-19 susceptibility identified several major patterns: i. a large impact of genetically determined innate immune responses in COVID-19, with ii. increased susceptibility for severe disease in individuals with defective cytokine production; iii. genetic susceptibility related to ABO blood groups is probably mediated through the von Willebrand factor (VWF) and endothelial dysfunction. We further validated these identified associations at transcript and protein levels by using independent disease cohorts. These insights allow a physiological understanding of genetic susceptibility to severe COVID-19, and indicate pathways that could be targeted for prevention and therapy.

Independent and Interactive Effects of Genetic Background and Sex on Tissue Metabolomes of Adipose, Skeletal Muscle, and Liver in Mice

Genetics play an important role in the development of metabolic diseases. However, the relative influence of genetic variation on metabolism is not well defined, particularly in tissues, where metabolic dysfunction that leads to disease occurs. We used inbred strains of laboratory mice to evaluate the impact of genetic variation on the metabolomes of tissues that play central roles in metabolic diseases. We chose a set of four common inbred strains that have different levels of susceptibility to obesity, insulin resistance, and other common metabolic disorders. At the ages used, and under standard husbandry conditions, these lines are not overtly diseased. Using global metabolomics profiling, we evaluated water-soluble metabolites in liver, skeletal muscle, and adipose from A/J, C57BL/6J, FVB/NJ, and NOD/ShiLtJ mice fed a standard mouse chow diet. We included both males and females to assess the relative influence of strain, sex, and strain-by-sex interactions on metabolomes. The mice were also phenotyped for systems level traits related to metabolism and energy expenditure. Strain explained more variation in the metabolite profile than did sex or its interaction with strain across each of the tissues, especially in liver. Purine and pyrimidine metabolism and pathways related to amino acid metabolism were identified as pathways that discriminated strains across all three tissues. Based on the results from ANOVA, sex and sex-by-strain interaction had modest influence on metabolomes relative to strain, suggesting that the tissue metabolome remains largely stable across sexes consuming the same diet. Our data indicate that genetic variation exerts a fundamental influence on tissue metabolism.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Metabolomics Meets Nutritional Epidemiology: Harnessing the Potential in Metabolomics Data

Traditionally, nutritional epidemiology is the study of the relationship between diet and health and disease in humans at the population level. Commonly, the exposure of interest is food intake. In recent years, nutritional epidemiology has moved from a "black box" approach to a systems approach where genomics, metabolomics and proteomics are providing novel insights into the interplay between diet and health. In this context, metabolomics is emerging as a key tool in nutritional epidemiology. The present review explores the use of metabolomics in nutritional epidemiology. In particular, it examines the role that food-intake biomarkers play in addressing the limitations of self-reported dietary intake data and the potential of using metabolite measurements in assessing the impact of diet on metabolic pathways and physiological processes. However, for full realisation of the potential of metabolomics in nutritional epidemiology, key challenges such as robust biomarker validation and novel methods for new metabolite identification need to be addressed. The synergy between traditional epidemiologic approaches and metabolomics will facilitate the translation of nutritional epidemiologic evidence to effective precision nutrition.