Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease.

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Thermoelectric transport properties of Si, SiGe, and silicide CMOS-compatible thin films.

Characterization of thermoelectric transport properties for temperature sensing, cooling, and energy harvesting applications is necessary for a reliable device performance in progressively minimized computer chips. In this contribution, we present a fully automated thermovoltage and sheet resistance measurement setup, which is calibrated and tested for the production of silicon- and silicon-germanium-doped as well as silicide complementary metal-oxide-semiconductor-compatible thin films. A LabVIEW-programmed software application automatically controls the measurement and recording of thermovoltages at individually defined temperature set points. The setup maps average temperature and temperature differences simultaneously in the regime from 40 to 70 °C. The Seebeck coefficient calculated by means of the inversion method was used to eliminate the offset voltage influence. Finally, we present and discuss the Seebeck coefficient as well as the sheet resistance for application-specific different temperature set points of several doped poly-Si, poly-SiGe, and silicides.

Causal inference on microbiome-metabolome relations in observational host-microbiome data via in silico in vivo association pattern analyses.

Understanding the effects of the microbiome on the host's metabolism is core to enlightening the role of the microbiome in health and disease. Herein, we develop the paradigm of in silico in vivo association pattern analyses, combining microbiome metabolome association studies with in silico constraint-based community modeling. Via theoretical dissection of confounding and causal paths, we show that in silico in vivo association pattern analyses allow for causal inference on microbiome-metabolome relations in observational data. We justify the corresponding theoretical criterion by structural equation modeling of host-microbiome systems, integrating deterministic microbiome community modeling into population statistics approaches. We show the feasibility of our approach on a published multi-omics dataset (n = 347), demonstrating causal microbiome-metabolite relations for 26 out of 54 fecal metabolites. In summary, we generate a promising approach for causal inference in metabolic host-microbiome interactions by integrating hypothesis-free screening association studies with knowledge-based in silico modeling.

Whole-body modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer's disease.

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilized whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalized models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients.

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite-metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity.

Longitudinal flux balance analyses of a patient with episodic colonic inflammation reveals microbiome metabolic dynamics.

We report the first use of constraint-based microbial community modeling on a single individual with episodic inflammation of the gastrointestinal tract, who has a well documented set of colonic inflammatory biomarkers, as well as metagenomically-sequenced fecal time series covering seven dates over 16 months. Between the first two time steps the individual was treated with both steroids and antibiotics. Our methodology enabled us to identify numerous time-correlated microbial species and metabolites. We found that the individual's dynamical microbial ecology in the disease state led to time-varying in silico overproduction, compared to healthy controls, of more than 24 biologically important metabolites, including methane, thiamine, formaldehyde, trimethylamine N-oxide, folic acid, serotonin, histamine, and tryptamine. The microbe-metabolite contribution analysis revealed that some Dialister species changed metabolic pathways according to the inflammation phases. At the first time point, characterized by the highest levels of serum (complex reactive protein) and fecal (calprotectin) inflammation biomarkers, they produced L-serine or formate. The production of the compounds, through a cascade effect, was mediated by the interaction with pathogenic Escherichia coli strains and Desulfovibrio piger. We integrated the microbial community metabolic models of each time point with a male whole-body, organ-resolved model of human metabolism to track the metabolic consequences of dysbiosis at different body sites. The presence of D. piger in the gut microbiome influenced the sulfur metabolism with a domino effect affecting the liver. These results revealed large longitudinal variations in an individual's gut microbiome ecology and metabolite production, potentially impacting other organs in the body. Future simulations with more time points from an individual could permit us to assess how external drivers, such as diet change or medical interventions, drive microbial community dynamics.

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.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

NMR Metabolomics Reveal Urine Markers of Microbiome Diversity and Identify Benzoate Metabolism as a Mediator between High Microbial Alpha Diversity and Metabolic Health.

Microbial metabolites measured using NMR may serve as markers for physiological or pathological host-microbe interactions and possibly mediate the beneficial effects of microbiome diversity. Yet, comprehensive analyses of gut microbiome data and the urine NMR metabolome from large general population cohorts are missing. Here, we report the associations between gut microbiota abundances or metrics of alpha diversity, quantified from stool samples using 16S rRNA gene sequencing, with targeted urine NMR metabolites measures from 951 participants of the Study of Health in Pomerania (SHIP). We detected significant genus-metabolite associations for hippurate, succinate, indoxyl sulfate, and formate. Moreover, while replicating the previously reported association between hippurate and measures of alpha diversity, we identified formate and 4-hydroxyphenylacetate as novel markers of gut microbiome alpha diversity. Next, we predicted the urinary concentrations of each metabolite using genus abundances via an elastic net regression methodology. We found profound associations of the microbiome-based hippurate prediction score with markers of liver injury, inflammation, and metabolic health. Moreover, the microbiome-based prediction score for hippurate completely mediated the clinical association pattern of microbial diversity, hinting at a role of benzoate metabolism underlying the positive associations between high alpha diversity and healthy states. In conclusion, large-scale NMR urine metabolomics delivered novel insights into metabolic host-microbiome interactions, identifying pathways of benzoate metabolism as relevant candidates mediating the beneficial health effects of high microbial alpha diversity.

Early-Life Adversity Leaves Its Imprint on the Oral Microbiome for More Than 20 Years and Is Associated with Long-Term Immune Changes.

The early-life microbiome (ELM) interacts with the psychosocial environment, in particular during early-life adversity (ELA), defining life-long health trajectories. The ELM also plays a significant role in the maturation of the immune system. We hypothesised that, in this context, the resilience of the oral microbiomes, despite being composed of diverse and distinct communities, allows them to retain an imprint of the early environment. Using 16S amplicon sequencing on the EpiPath cohort, we demonstrate that ELA leaves an imprint on both the salivary and buccal oral microbiome 24 years after exposure to adversity. Furthermore, the changes in both communities were associated with increased activation, maturation, and senescence of both innate and adaptive immune cells, although the interaction was partly dependent on prior herpesviridae exposure and current smoking. Our data suggest the presence of multiple links between ELA, Immunosenescence, and cytotoxicity that occur through long-term changes in the microbiome.

Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine.

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression.

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production.

Characterizing the metabolic functions of the gut microbiome in health and disease is pivotal for translating alterations in microbial composition into clinical insights. Two major analysis paradigms have been used to explore the metabolic functions of the microbiome but not systematically integrated with each other: statistical screening approaches, such as metabolome-microbiome association studies, and computational approaches, such as constraint-based metabolic modeling. To combine the strengths of the two analysis paradigms, we herein introduce a set of theoretical concepts allowing for the population statistical treatment of constraint-based microbial community models. To demonstrate the utility of the theoretical framework, we applied it to a public metagenomic dataset consisting of 365 colorectal cancer (CRC) cases and 251 healthy controls, shining a light on the metabolic role of Fusobacterium spp. in CRC. We found that (1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacterium spp., (2) acetate and butyrate production potentials were lowered in CRC, and (3) Fusobacterium spp. presence had large negative ecological effects on community butyrate production in CRC cases and healthy controls. Validating the model predictions against fecal metabolomics, the in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modeling, this study provides insights into the metabolic role of Fusobacterium spp. in the gut, while providing a proof of concept for the validity of constraint-based microbial community modeling.

Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis.

Inflammatory bowel diseases, such as Crohn's Disease, are characterised by an altered blood and faecal metabolome, and changes in gut microbiome composition. Here, we present an efficient, scalable, tractable systems biology framework to mechanistically link microbial strains and faecal metabolites. We retrieve strain-level relative abundances from metagenomics data from a cohort of paediatric Crohn's Disease patients with and without dysbiosis and healthy control children and construct and interrogate a personalised microbiome model for each sample. Predicted faecal secretion profiles and strain-level contributions to each metabolite vary broadly between healthy, dysbiotic, and non-dysbiotic microbiomes. The reduced microbial diversity in IBD results in reduced numbers of secreted metabolites, especially in sulfur metabolism. We demonstrate that increased potential to synthesise amino acids is linked to Proteobacteria contributions, in agreement with experimental observations. The established modelling framework yields testable hypotheses that may result in novel therapeutic and dietary interventions targeting the host-gut microbiome-diet axis.

Broad Metabolome Alterations Associated with the Intake of Oral Contraceptives Are Mediated by Cortisol in Premenopausal Women.

The use of oral contraceptives (OCs) has been associated with elevated blood cortisol concentrations. However, metabolic downstream effects of OC intake are not well described. Here, we aimed to determine if the blood metabolome is associated with the use of OCs and to estimate if these associations might be statistically mediated by serum cortisol concentrations. Plasma metabolites measured with the Biocrates AbsoluteIDQ p180 Kit and serum cortisol concentrations measured by an immunoassay were determined in 391 premenopausal women (116 OC users) participating in two independent cohorts of the Study of Health in Pomerania (SHIP). After correction for multiple testing, 27 metabolites were significantly associated with OC intake in SHIP-TREND (discovery cohort), of which 25 replicated in SHIP-2. Inter alia, associated metabolites included 12 out of 38 phosphatidylcholines with diacyl residue, 7 out of 14 lysophosphatidylcholines and 5 out of 21 amino acids. The associations with phosphatidylcholines were statistically mediated by cortisol, whereas lysophosphatidylcholines showed no mediation effect. The results represent a step toward a better understanding of the metabolic consequences of OC intake. Connecting cortisol with metabolic consequences of OC intake could help to understand the mechanisms underlying adverse effects. The blood metabolome may serve as a biomarker for identifying users at high risk for developing such adverse effects.

Methanogenic granule growth and development is a continual process characterized by distinct morphological features.

Up-flow anaerobic bioreactors are widely applied for high-rate digestion of industrial wastewaters and rely on formation, and retention, of methanogenic granules, comprising of dense, fast-settling, microbial aggregates (approx. 0.5-4.0 mm in diameter). Granule formation (granulation) mechanisms have been reasonably well hypothesized and documented. However, this study used laboratory-scale bioreactors, inoculated with size-separated granular sludge to follow new granule formation, maturation, disintegration and re-formation. Temporal size profiles, volatile solids content, settling velocity, and ultrastructure of granules were determined from each of four bioreactors inoculated only with small granules, four with only large granules, and four with a full complement of naturally-size-distributed granules. Constrained granule size profiles shifted toward the natural distribution, which was associated with maximal bioreactor performance. Distinct morphological features characterized different granule sizes and biofilm development stages, including 'young', 'juvenile', 'mature' and 'old'. The findings offer opportunities toward optimizing management of high-rate, anaerobic digesters by shedding light on the rates of granule growth, the role of flocculent sludge in granulation and how shifting size distributions should be considered when setting upflow velocities.

Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism.

Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

Attention Deficit Hyperactivity Disorder in Children and Adults: A Population Survey on Public Beliefs.

Objective: To investigate beliefs and attitudes of the public toward attention deficit hyperactivity disorder (ADHD) in children and adults. Method: In a representative population survey in Germany (N = 1,008) using computer-assisted telephone interviews, we asked participants about causal beliefs, illness recognition, treatment recommendations, and beliefs about ADHD, presenting an unlabelled vignette of a child or an adult with ADHD. Results: The most frequently endorsed causal beliefs for the depicted child with ADHD were "TV or Internet," "lack of parental affection," and "broken home." In comparison with the child vignette, biological causal beliefs were endorsed more often after the adult vignette. In the child vignette, 66% advised against a treatment with stimulant medication. About 90% of respondents had heard of ADHD. Of those, 20% said they believed ADHD to be not a real disease. Conclusion: Beliefs of the German public partly contradict evidence and should be considered in therapeutical and public contexts.