Fecal 1H-NMR Metabolomics: A Comparison of Sample Preparation Methods for NMR and Novel in Silico Baseline Correction

Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy

We report the development of a software program, called MagMet-F, that automates the processing and quantification of 1D 1 H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1 H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1 H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet-F. The fitting of these metabolites by MagMet-F was iteratively optimized to replicate manual profiling. We validated MagMet-F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet-F's profiling performance to two other open-access NMR profiling tools, Bayesil and Batman. MagMet-F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10-20× longer. We have implemented MagMet-F as a freely accessible web server to enable automated, fast and convenient 1D 1 H NMR spectral profiling of fecal samples. MagMet-F is available at https://www.magmet.ca.

Comparative Analysis of the Temporal Impacts of Corticosterone and Simulated Production Stressors on the Metabolome of Broiler Chickens

The impact of physiological stress on the metabolome of breast muscle, liver, kidney, and hippocampus was investigated in Ross 308 broiler chicks. Simulated on-farm stressors were compared to a corticosterone model of physiological stress. The three different stressors investigated were: (i) corticosterone at a dose of 15 mg/kg of feed; (ii) heat treatment of 36 °C and 40% RH for 8 h per day; and (iii) isolation for 1 h per day. Liver, kidney, breast muscle, and hippocampus samples were taken after 2, 4, 6, and 8 days of stress treatment, and subjected to untargeted ¹H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis to provide insights on how stress can modulate metabolite profiles and biomarker discovery. Many of the metabolites that were significantly altered in tissues were amino acids, with glycine and alanine showing promise as candidate biomarkers of stress. Corticosterone was shown to significantly alter alanine, aspartate, and glutamate metabolism in the liver, breast, and hippocampus, while isolation altered the same pathways, but only in the kidneys and hippocampus. Isolation also significantly altered the glycine, serine, and threonine metabolism pathway in the liver and breast, while the same pathway was significantly altered by heat in the liver, kidneys, and hippocampus. The study's findings support corticosterone as a model of stress. Moreover, a number of potential metabolite biomarkers were identified in chicken tissues, which may allow producers to effectively monitor stress and to objectively develop and evaluate on-farm mitigations, including practices that reduce stress and enhance bird health.

Multi-omic Analysis of the Gut Microbiome in Rats with Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Evidence supports that the gut microbiota and bacteria-dependent metabolites influence the maintenance of epileptic brain activity. However, the alterations in the gut microbiota between epileptic versus healthy individuals are poorly understood. We used a multi-omic approach to evaluate the changes in the composition of gut metagenome as well in the fecal metabolomic profile in rats before and after being submitted to status epilepticus (SE)-induced temporal lobe epilepsy (TLE). The 16S ribosomal RNA (rRNA) sequencing of fecal samples coupled to bioinformatic analysis revealed taxonomic, compositional, and functional shifts in epileptic rats. The species richness (Chao1 index) was significantly lower in the post-TLE group, and the β-diversity analysis revealed clustering separated from the pre-TLE group. The taxonomic abundance analysis showed a significant increase of phylum Desulfobacterota and a decrease of Patescibacteria in the post-TLE group. The DESEq2 and LEfSe analysis resulted in 18 genera significantly enriched between post-TLE and pre-TLE groups at the genus level. We observed that epileptic rats present a peculiar metabolic phenotype, including a lower concentration of D-glucose and L-lactic acid and a higher concentration of L-glutamic acid and glycine. The microbiota-host metabolic correlation analysis showed that the genera differentially abundant in post-TLE rats are associated with the altered metabolites, especially the proinflammatory Desulfovibrio and Marvinbryantia, which were enriched in epileptic animals and positively correlated with these excitatory neurotransmitters and carbohydrate metabolites. Therefore, our data revealed a correlation between dysbacteriosis in epileptic animals and fecal metabolites that are known to be relevant for maintaining epileptic brain activity by enhancing chronic inflammation, an excitatory-inhibitory imbalance, and/or a metabolic disturbance. These data are promising and suggest that targeting the gut microbiota could provide a novel avenue for preventing and treating acquired epilepsy. However, the causal relationship between these microbial/metabolite components and the SRS occurrence still needs further exploration.

1H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where ¹H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using ¹H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r² = 0.423, p < 2.2 × 10^-16). This correlation was significantly reduced (r² = 0.163, p < 2.2 × 10^-16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in ¹H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

Feather pulp: a novel substrate useful for proton nuclear magnetic resonance spectroscopy metabolomics and biomarker discovery

Noninvasive biomarkers of stress that are predictive of poultry health are needed. Feather pulp is highly vascularized and represents a potential source of biomarkers that has not been extensively explored. We investigated the feasibility and use of feather pulp for novel biomarker discovery using ¹H-Nuclear Magnetic Resonance Spectroscopy (NMR)-based metabolomics. To this end, high quality NMR metabolomic spectra were obtained from chicken feather pulp extracted using either ultrafiltration (UF) or Bligh-Dyer methanol-chloroform (BD) methods. In total, 121 and 160 metabolites were identified using the UF and BD extraction methods, respectively, with 71 of these common to both methods. The metabolome of feather pulp differed in broiler breeders that were 1-, 23-, and 45-wk-of-age. Moreover, feather pulp was more difficult to obtain from older birds, indicating that age must be considered when targeting feather pulp as a source of biomarkers. The metabolomic profile of feather pulp obtained from 12-day-old broilers administered corticosterone differed from control birds, indicating that the metabolome of feather pulp was sensitive to induced physiological stress. A comparative examination of feather pulp and serum in broilers revealed that the feather pulp metabolome differed from that of serum but provided more information. The study findings show that metabolite biomarkers in chicken feather pulp may allow producers to effectively monitor stress, and to objectively develop and evaluate on-farm mitigations, including practices that reduce stress and enhance bird health.