Trachymyrmex septentrionalis ants promote fungus garden hygiene using Trichoderma-derived metabolite cues.

Fungus-growing ants depend on a fungal mutualist that can fall prey to fungal pathogens. This mutualist is cultivated by these ants in structures called fungus gardens. Ants exhibit weeding behaviors that keep their fungus gardens healthy by physically removing compromised pieces. However, how ants detect diseases of their fungus gardens is unknown. Here, we applied the logic of Koch's postulates using environmental fungal community gene sequencing, fungal isolation, and laboratory infection experiments to establish that Trichoderma spp. can act as previously unrecognized pathogens of Trachymyrmex septentrionalis fungus gardens. Our environmental data showed that Trichoderma are the most abundant noncultivar fungi in wild T. septentrionalis fungus gardens. We further determined that metabolites produced by Trichoderma induce an ant weeding response that mirrors their response to live Trichoderma. Combining ant behavioral experiments with bioactivity-guided fractionation and statistical prioritization of metabolites in Trichoderma extracts demonstrated that T. septentrionalis ants weed in response to peptaibols, a specific class of secondary metabolites known to be produced by Trichoderma fungi. Similar assays conducted using purified peptaibols, including the two previously undescribed peptaibols trichokindins VIII and IX, suggested that weeding is likely induced by peptaibols as a class rather than by a single peptaibol metabolite. In addition to their presence in laboratory experiments, we detected peptaibols in wild fungus gardens. Our combination of environmental data and laboratory infection experiments strongly support that peptaibols act as chemical cues of Trichoderma pathogenesis in T. septentrionalis fungus gardens.

ReDU: a framework to find and reanalyze public mass spectrometry data.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one's own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Furan fatty acid metabolite in newborns predicts risk of asthma.

BACKGROUND: Intake of fish-oil and fatty fish during pregnancy has been shown to reduce the risk of childhood asthma but biomarkers of such intake are lacking. OBJECTIVE: To establish biomarkers of prenatal fish-oil exposure from newborn dry blood spot metabolomics profiles and assess their relevance for childhood asthma risk stratification. METHODS: The Danish COPSAC2010 mother-child cohort was utilized to investigate the effect of a double-blinded randomized controlled trial of fish-oil supplementation during pregnancy on dry blood spot liquid-chromatography mass spectrometry-based metabolomics profiles of 677 newborns. We thereafter investigated the association between fish-oil associated biomarkers in the newborn and development of asthma-related outcomes. Replication was sought in the independent observational COPSAC2000 cohort with 387 newborn metabolomics profiles. RESULTS: The newborn metabolomics profiles differed between children in the fish-oil vs. placebo group in COPSAC2010 (area under the receiver operator curve = 0.94 ± 0.03, p < .001). The fish-oil metabolomics profile and the top biomarker, 3-carboxy-4-methyl-5-propyl-2-furan propanoic acid (CMPF) were both associated with a decreased risk of asthma by age 6 years (HR = 0.89, p = .002 and HR = 0.67, p = .005, respectively). In COPSAC2000 , newborn CMPF level was also inversely associated with asthma risk by age 6 years (HR = 0.69, p = .01). Troublesome lung symptoms and common infections in the first 3 years were also inversely associated with newborn CMPF levels in both cohorts. CONCLUSIONS: Newborn children's blood levels of the furan fatty acid metabolite CMPF reflect fish-oil and fatty fish intake during pregnancy and are associated with a lower risk of asthma across two cohorts, which could aid newborn screening for childhood asthma.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Diet-associated vertically transferred metabolites and risk of asthma, allergy, eczema, and infections in early childhood.

BACKGROUND: Evidence suggests maternal pregnancy dietary intake and nutrition in the early postnatal period to be of importance for the newborn child's health. However, studies investigating diet-related metabolites transferred from mother to child on disease risk in childhood are lacking. We sought to investigate the influence of vertically transferred metabolites on risk of atopic diseases and infections during preschool age. METHODS: In the Danish population-based COPSAC2010 mother-child cohort, information on 10 diet-related vertically transferred metabolites from metabolomics profiles of dried blood spots (DBS) at age 2-3 days was analyzed in relation to the risk of childhood asthma, allergy, eczema, and infections using principal component and single metabolite analyses. RESULTS: In 678 children with DBS measurements, a coffee-related metabolite profile reflected by principal component 1 was inversely associated with risk of asthma (odds ratio (95% CI) 0.78 (0.64; 0.95), p = .014) and eczema at age 6 years (0.79 (0.65; 0.97), p = .022). Furthermore, increasing stachydrine (fruit-related), 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (fish-related), and ergothioneine (fruit-, green vegetables-, and fish-related) levels were all significantly associated with reduced risks of infections at age 0-3 years (p < .05). CONCLUSION: This study demonstrates associations between pregnancy diet-related vertically transferred metabolites measured in children in early life and risk of atopic diseases and infections in childhood. The specific metabolites associated with a reduced disease risk in children may contribute to the characterization of a healthy nutritional profile in pregnancy using a metabolomics-based unbiased tool for predicting childhood health.

Multi-omics to predict changes during cold pressor test.

BACKGROUND: The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each 'omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least squares [PLS] regression analysis) and all 'omic levels were combined using an integrative multi-omics approach, all using the paired-sample design. RESULTS: We showed that PCA was not able to discriminate time points, while PLS did significantly distinguish time points using metabolomics and/or transcriptomic data, but not using conventional physiological measures. Transcriptomic and metabolomic data revealed at feature-, systems- and integrative- level biologically relevant processes involved during CPT, e.g. lipid metabolism and stress response. CONCLUSION: Multi-omics strategies have a great potential in pain research, both at feature- and systems- level. Therefore, they should be exploited in intervention studies, such as pain provocation tests, to gain knowledge on the biological mechanisms involved in complex traits.

Neonatal metabolome of caesarean section and risk of childhood asthma.

BACKGROUND: Birth by caesarean section is linked to an increased risk of developing asthma, but the underlying mechanisms are unclear. OBJECTIVE: To elucidate the link between birth by caesarean section and asthma using newborn metabolomic profiles and integrating early-life gut microbiome data and cord blood immunology. METHODS: We investigated the influence of caesarean section on liquid chromatography mass spectrometry metabolomic profiles of dried blood spots from newborns of the two independent Copenhagen Prospective Studies on Asthma in Childhood cohorts, i.e. COPSAC2010 (n=677) and COPSAC2000 (n=387). We assessed the associations between the caesarean section metabolic profile, gut microbiome data and frequency of cord blood regulatory T-cells (Tregs) at 1 week of age. RESULTS: In COPSAC2010, a partial least square discriminant analysis model showed that children born by caesarean section versus natural delivery had different metabolic profiles (area under the curve (AUC)=0.77, p=2.2×10-16), which was replicated in COPSAC2000 (AUC=0.66, p=1.2×10-5). The metabolic profile of caesarean section was significantly associated with an increased risk of asthma at school age in both COPSAC2010 (p=0.03) and COPSAC2000 (p=0.005). Caesarean section was associated with lower abundance of tryptophan, bile acid and phenylalanine metabolites, indicative of a perturbed gut microbiota. Furthermore, gut bacteria dominating after natural delivery, i.e. Bifidobacterium and Bacteroides were correlated with caesarean section-discriminative microbial metabolites, suggesting maternal microbial transmission during birth regulating the newborn's metabolism. Finally, the caesarean section metabolic profile was associated with frequency of cord blood Tregs. CONCLUSIONS: These findings propose that caesarean section programmes the risk of childhood asthma through perturbed immune responses and gut microbial colonisation patterns reflected in the blood metabolome at birth.

Gestational age-dependent development of the neonatal metabolome.

BACKGROUND: Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome. METHODS: Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age. RESULTS: A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine. CONCLUSIONS: Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders. IMPACT: A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.

Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae.

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well-known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations for the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.

Initial Development toward Non-Invasive Drug Monitoring via Untargeted Mass Spectrometric Analysis of Human Skin.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC-MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC-MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin.

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra.

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented, giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses, termed Mass2Motifs, that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of (i) combinatorial in silico matching of experimental mass features to substructures of candidate molecules, and (ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present MotifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database, we can more quickly gain insight into the constituents of complex mixtures. This will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

Mass spectrometry in plant metabolomics strategies: from analytical platforms to data acquisition and processing.

Covering: up to 2013. Plant metabolomics is a relatively recent research field that has gained increasing interest in the past few years. Up to the present day numerous review articles and guide books on the subject have been published. This review article focuses on the current applications and limitations of the modern mass spectrometry techniques, especially in combination with electrospray ionisation (ESI), an ionisation method which is most commonly applied in metabolomics studies. As a possible alternative to ESI, perspectives on matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) in metabolomics studies are introduced, a method which still is not widespread in the field. In metabolomics studies the results must always be interpreted in the context of the applied sampling procedures as well as data analysis. Different sampling strategies are introduced and the importance of data analysis is illustrated in the example of metabolic network modelling.

Proteomic identification of beer brewing products in the ground layer of Danish Golden Age paintings.

The application of mass spectrometry-based proteomics to artworks provides accurate and detailed characterization of protein-based materials used in their production. This is highly valuable to plan conservation strategies and reconstruct the artwork's history. In this work, the proteomic analysis of canvas paintings from the Danish Golden Age led to the confident identification of cereal and yeast proteins in the ground layer. This proteomic profile points to a (by-)product of beer brewing, in agreement with local artists' manuals. The use of this unconventional binder can be connected to the workshops within the Royal Danish Academy of Fine Arts. The mass spectrometric dataset generated from proteomics was also processed with a metabolomics workflow. The spectral matches observed supported the proteomic conclusions, and, in at least one sample, suggested the use of drying oils. These results highlight the value of untargeted proteomics in heritage science, correlating unconventional artistic materials with local culture and practices.

Effects of Long-Term Storage on the Biobanked Neonatal Dried Blood Spot Metabolome.

Over 2.5 million neonatal dried blood spots (DBS) are stored at the Danish National Biobank. These samples offer extraordinary possibilities for metabolomics research, including prediction of disease and understanding of underlying molecular mechanisms of disease development. Nevertheless, Danish neonatal DBS have been little explored in metabolomics studies. One question that remains underinvestigated is the long-term stability of the large number of metabolites typically assessed in untargeted metabolomics over long time periods of storage. Here, we investigate temporal trends of metabolites measured in 200 neonatal DBS collected over a time course of 10 years, using an untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) based metabolomics protocol. We found that a majority (71%) of the metabolome was stable during 10 years of storage at -20 °C. However, we found decreasing trends for lipid-related metabolites, such as glycerophosphocholines and acylcarnitines. A few metabolites, including glutathione and methionine, may be strongly influenced by storage, with changes in metabolite levels up to 0.1-0.2 standard deviation units per year. Our findings indicate that untargeted metabolomics of DBS samples, with long-term storage in biobanks, is suitable for retrospective epidemiological studies. We identify metabolites whose stability in DBS should be closely monitored in future studies of DBS samples with long-term storage.

Metabolic signature of the pathogenic 22q11.2 deletion identifies carriers and provides insight into systemic dysregulation.

Large deletions at chromosome 22q11.2 are known to cause severe clinical conditions collectively known as 22q11.2 deletion syndrome. Notwithstanding the pathogenicity of these deletions, affected individuals are typically diagnosed in late childhood or early adolescence, and little is known of the molecular signaling cascades and biological consequences immediately downstream of the deleted genes. Here, we used targeted metabolomics to compare neonatal dried blood spot samples from 203 individuals clinically identified as carriers of a deletion at chromosome 22q11.2 with 203 unaffected individuals. A total of 173 metabolites were successfully identified and used to inform on systemic dysregulation caused by the genomic lesion and to discriminate carriers from non-carriers. We found 84 metabolites to be differentially abundant between carriers and non-carriers of the 22q11.2 deletion. A predictive model based on all 173 metabolites achieved high Accuracy (89%), Area Under the Curve (93%), F1 (88%), Positive Predictive Value (94%), and Negative Predictive Value (84%) with tyrosine and proline having the highest individual contributions to the model as well as the highest interaction strength. Targeted metabolomics provides insight into the molecular consequences possibly contributing to the pathology underlying the clinical manifestations of the 22q11 deletion and is an easily applicable approach to first-pass screening for carrier status of the 22q11 to prompt subsequent verification of the genomic diagnosis.

Multi-omic analyses of triptan-treated migraine attacks gives insight into molecular mechanisms.

Migraine is a common, polygenic disorder that is characterized by moderate to severe headache attacks. Migraine attacks are commonly treated with triptans, i.e. serotonin receptor agonists. However, triptans are effective in ~ 60% of the population, and the mechanisms of triptans are debated. Here, we aim to expose the mechanisms of triptan using metabolomics and transcriptomics in spontaneous migraine attacks. We collected temporal multi-omics profiles on 24 migraine patients, using samples collected at a migraine attack, 2 h after treatment with a triptan, when headache-free, and after a cold-pressor test. Differential metabolomic analysis was performed to find metabolites associated with treatment. Their effect was further investigated using correlation analysis and a machine learning approach. We found three differential metabolites: cortisol, sumatriptan and glutamine. The change in sumatriptan levels correlated with a change in GNAI1 and VIPR2 gene expression, both known to regulate cAMP levels. Furthermore, we found fatty acid oxidation to be affected, a mechanism known to be involved in migraine but not previously found in relation to triptans. In conclusion, using an integrative approach we find evidence for a role of glutamine, cAMP regulation, and fatty acid oxidation in the molecular mechanisms of migraine and/or the effect of triptans.

The Molecular Effect of Wearing Silver-Threaded Clothing on the Human Skin.

With growing awareness that what we put in and on our bodies affects our health and wellbeing, little is still known about the impact of textiles on the human skin. Athletic wear often uses silver threading to improve hygiene, but little is known about its effect on the body's largest organ. In this study, we investigated the impact of such clothing on the skin's chemistry and microbiome. Samples were collected from different body sites of a dozen volunteers over the course of 12 weeks. The changes induced by the antibacterial clothing were specific for individuals, but more so defined by gender and body site. Unexpectedly, the microbial biomass on skin increased in the majority of the volunteers when wearing silver-threaded T-shirts. Although the most abundant taxa remained unaffected, silver caused an increase in diversity and richness of low-abundant bacteria and a decrease in chemical diversity. Both effects were mainly observed for women. The hallmark of the induced changes was an increase in the abundance of various monounsaturated fatty acids (MUFAs), especially in the upper back. Several microbe-metabolite associations were uncovered, including Cutibacterium, detected in the upper back area, which was correlated with the distribution of MUFAs, and Anaerococcus spp. found in the underarms, which were associated with a series of different bile acids. Overall, these findings point to a notable impact of the silver-threaded material on the skin microbiome and chemistry. We observed that relatively subtle changes in the microbiome result in pronounced shifts in molecular composition. IMPORTANCE The impact of silver-threaded material on human skin chemistry and microbiome is largely unknown. Although the most abundant taxa remained unaffected, silver caused an increase in diversity and richness of low-abundant bacteria and a decrease in chemical diversity. The major change was an increase in the abundance of various monounsaturated fatty acids that were also correlated with Cutibacterium. Additionally, Anaerococcus spp., found in the underarms, were associated with different bile acids in the armpit samples. Overall, the impact of the silver-threaded clothing was gender and body site specific.

Vertical Transfer of Metabolites Detectable from Newborn's Dried Blood Spot Samples Using UPLC-MS: A Chemometric Study.

The pregnancy period and first days of a newborn's life is an important time window to ensure a healthy development of the baby. This is also the time when the mother and her baby are exposed to the same environmental conditions and intake of nutrients, which can be determined by assessing the blood metabolome. For this purpose, dried blood spots (DBS) of newborns are a valuable sampling technique to characterize what happens during this important mother-child time window. We used metabolomics profiles from DBS of newborns (age 2-3 days) and maternal plasma samples at gestation week 24 and postpartum week 1 from n=664 mother-child pairs of the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) cohort, to study the vertical mother-child transfer of metabolites. Further, we investigated how persistent the metabolites are from the newborn and up to 6 months, 18 months, and 6 years of age. Two hundred seventy two metabolites from UPLC-MS (Ultra Performance Liquid Chromatography-Mass Spectrometry) analysis of DBS and maternal plasma were analyzed using correlation analysis. A total of 11 metabolites exhibited evidence of transfer (R>0.3), including tryptophan betaine, ergothioneine, cotinine, theobromine, paraxanthine, and N6-methyllysine. Of these, 7 were also found to show persistence in their levels in the child from birth to age 6 years. In conclusion, this study documents vertical transfer of environmental and food-derived metabolites from mother to child and tracking of those metabolites through childhood, which may be of importance for the child's later health and disease.

A multi-omics approach unravels metagenomic and metabolic alterations of a probiotic and synbiotic additive in rainbow trout (Oncorhynchus mykiss).

BACKGROUND: Animal protein production is increasingly looking towards microbiome-associated services such as the design of new and better probiotic solutions to further improve gut health and production sustainability. Here, we investigate the functional effects of bacteria-based pro- and synbiotic feed additives on microbiome-associated functions in relation to growth performance in the commercially important rainbow trout (Oncorhynchus mykiss). We combine complementary insights from multiple omics datasets from gut content samples, including 16S bacterial profiling, whole metagenomes, and untargeted metabolomics, to investigate bacterial metagenome-assembled genomes (MAGs) and their molecular interactions with host metabolism. RESULTS: Our findings reveal that (I) feed additives changed the microbiome and that rainbow trout reared with feed additives had a significantly reduced relative abundance of the salmonid related Candidatus Mycoplasma salmoninae in both the mid and distal gut content, (II) genome resolved metagenomics revealed that alterations of microbial arginine biosynthesis and terpenoid backbone synthesis pathways were directly associated with the presence of Candidatus Mycoplasma salmoninae, and (III) differences in the composition of intestinal microbiota among feed types were directly associated with significant changes of the metabolomic landscape, including lipids and lipid-like metabolites, amino acids, bile acids, and steroid-related metabolites. CONCLUSION: Our results demonstrate how the use of multi-omics to investigate complex host-microbiome interactions enable us to better evaluate the functional potential of probiotics compared to studies that only measure overall growth performance or that only characterise the microbial composition in intestinal environments. Video Abstract.