Poison, plants and Palaeolithic hunters. An analytical method to investigate the presence of plant poison on archaeological artefacts

In this paper we present the development of a method for the detection of toxic substances on ancient arrow points. The aim is to go back in time until the Palaeolithic period in order to determine if poisonous substances were used to enhance the hunting weapons. The ethnographic documentation demonstrates that hunters of every latitude poisoned their weapons with toxic substances derived from plants and occasionally from animals. This highlights that often the weapons would be rather ineffective if the tips were not poisoned. The fact that toxic substances were available and the benefits arising from their application on throwing weapons, suggests that this practice could be widespread also among prehistoric hunters. The project reviewed the research of the toxic molecules starting from current information on modern plants and working backwards through the ages with the study of ethnographic and historical weapons. This knowledge was then applied to the archaeological material collected from International museum collections. Results have shown that using this method it is possible to detect traces of toxic molecules with mass spectrometry (MS) and hyphenated chromatographic techniques even on samples older than one hundred years, which we consider a positive incentive to continue studying plant poisons on ancient hunting tools

Mapping the Variation of the Carrot Metabolome Using ¹H NMR Spectroscopy and Consensus PCA

Genetic variation is the most influential factor for carrot (Daucus carota L.) composition. However, difference in metabolite content between carrot varieties has not been described by NMR, although primary metabolites are important for human health and sensory properties. The aim of the present study was to investigate the effect of genotype on carrot metabolite composition using a ¹H NMR-based metabolomics approach. After extraction using aqueous and organic solvents, 25 hydrophilic metabolites, β-carotene, sterols, triacylglycerols, and phospholipids were detected. Multiblock PCA showed that three principal components could be identified for classification of the five carrot varieties using different spectroscopic regions and the results of the two solvent extraction methods as blocks. The varieties were characterized by differences in carbohydrate, amino acid, nucleotide, fatty acid, sterol, and β-carotene contents. ¹H NMR spectroscopy coupled with multiblock data analysis was an efficient and useful tool to map the carrot metabolome and identify genetic differences between varieties

Mapping the Variation of the Carrot Metabolome Using ¹H NMR Spectroscopy and Consensus PCA

Genetic variation is the most influential factor for carrot (Daucus carota L.) composition. However, difference in metabolite content between carrot varieties has not been described by NMR, although primary metabolites are important for human health and sensory properties. The aim of the present study was to investigate the effect of genotype on carrot metabolite composition using a ¹H NMR-based metabolomics approach. After extraction using aqueous and organic solvents, 25 hydrophilic metabolites, β-carotene, sterols, triacylglycerols, and phospholipids were detected. Multiblock PCA showed that three principal components could be identified for classification of the five carrot varieties using different spectroscopic regions and the results of the two solvent extraction methods as blocks. The varieties were characterized by differences in carbohydrate, amino acid, nucleotide, fatty acid, sterol, and β-carotene contents. ¹H NMR spectroscopy coupled with multiblock data analysis was an efficient and useful tool to map the carrot metabolome and identify genetic differences between varieties

Enhancing the Power of Liquid Chromatography–Mass Spectrometry-Based Urine Metabolomics in Negative Ion Mode by Optimization of the Additive

Untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics studies are usually carried out in both positive and negative ion modes; however, it is frequently ignored that the optimal conditions in positive ion mode and negative ion mode are often not the same. We carried out a systematic investigation on urine samples to evaluate the additive effects in negative ion mode. It was found that the widely used conditions, 0.1% formic acid (FA) and NH₄Ac at different pH, are far from the optimum for untargeted urine metabolomics studies. Compared to 0.1% FA, the use of 1 mM acetic acid (HAc) resulted in almost three times as many detected peaks (401 vs 148) and around five times the size of the peak area (33.55 × 10⁶ vs 6.47 × 10⁶). The remarkable improvement can be explained by two factors: (i) a significantly enhanced ionization efficiency due to the combination of an appropriate pH at around 4.0–4.5, the reducibility of H⁺, and the high gas-phase basicity of Ac^– and (ii) a reproducible LC separation due to an acceptable buffering capacity. Our study revealed the importance and necessity of additive optimization, which can be of benefit in related metabolomics studies

Enhancing the Power of Liquid Chromatography–Mass Spectrometry-Based Urine Metabolomics in Negative Ion Mode by Optimization of the Additive

Untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics studies are usually carried out in both positive and negative ion modes; however, it is frequently ignored that the optimal conditions in positive ion mode and negative ion mode are often not the same. We carried out a systematic investigation on urine samples to evaluate the additive effects in negative ion mode. It was found that the widely used conditions, 0.1% formic acid (FA) and NH₄Ac at different pH, are far from the optimum for untargeted urine metabolomics studies. Compared to 0.1% FA, the use of 1 mM acetic acid (HAc) resulted in almost three times as many detected peaks (401 vs 148) and around five times the size of the peak area (33.55 × 10⁶ vs 6.47 × 10⁶). The remarkable improvement can be explained by two factors: (i) a significantly enhanced ionization efficiency due to the combination of an appropriate pH at around 4.0–4.5, the reducibility of H⁺, and the high gas-phase basicity of Ac^– and (ii) a reproducible LC separation due to an acceptable buffering capacity. Our study revealed the importance and necessity of additive optimization, which can be of benefit in related metabolomics studies

Enhancing the Power of Liquid Chromatography–Mass Spectrometry-Based Urine Metabolomics in Negative Ion Mode by Optimization of the Additive

Untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics studies are usually carried out in both positive and negative ion modes; however, it is frequently ignored that the optimal conditions in positive ion mode and negative ion mode are often not the same. We carried out a systematic investigation on urine samples to evaluate the additive effects in negative ion mode. It was found that the widely used conditions, 0.1% formic acid (FA) and NH₄Ac at different pH, are far from the optimum for untargeted urine metabolomics studies. Compared to 0.1% FA, the use of 1 mM acetic acid (HAc) resulted in almost three times as many detected peaks (401 vs 148) and around five times the size of the peak area (33.55 × 10⁶ vs 6.47 × 10⁶). The remarkable improvement can be explained by two factors: (i) a significantly enhanced ionization efficiency due to the combination of an appropriate pH at around 4.0–4.5, the reducibility of H⁺, and the high gas-phase basicity of Ac^– and (ii) a reproducible LC separation due to an acceptable buffering capacity. Our study revealed the importance and necessity of additive optimization, which can be of benefit in related metabolomics studies

Enhancing the Power of Liquid Chromatography–Mass Spectrometry-Based Urine Metabolomics in Negative Ion Mode by Optimization of the Additive

Untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics studies are usually carried out in both positive and negative ion modes; however, it is frequently ignored that the optimal conditions in positive ion mode and negative ion mode are often not the same. We carried out a systematic investigation on urine samples to evaluate the additive effects in negative ion mode. It was found that the widely used conditions, 0.1% formic acid (FA) and NH₄Ac at different pH, are far from the optimum for untargeted urine metabolomics studies. Compared to 0.1% FA, the use of 1 mM acetic acid (HAc) resulted in almost three times as many detected peaks (401 vs 148) and around five times the size of the peak area (33.55 × 10⁶ vs 6.47 × 10⁶). The remarkable improvement can be explained by two factors: (i) a significantly enhanced ionization efficiency due to the combination of an appropriate pH at around 4.0–4.5, the reducibility of H⁺, and the high gas-phase basicity of Ac^– and (ii) a reproducible LC separation due to an acceptable buffering capacity. Our study revealed the importance and necessity of additive optimization, which can be of benefit in related metabolomics studies

Time-Saving Design of Experiment Protocol for Optimization of LC-MS Data Processing in Metabolomic Approaches

We describe a time-saving protocol for the processing of LC-MS-based metabolomics data by optimizing parameter settings in XCMS and threshold settings for removing noisy and low-intensity peaks using design of experiment (DoE) approaches including Plackett-Burman design (PBD) for screening and central composite design (CCD) for optimization. A reliability index, which is based on evaluation of the linear response to a dilution series, was used as a parameter for the assessment of data quality. After identifying the significant parameters in the XCMS software by PBD, CCD was applied to determine their values by maximizing the reliability and group indexes. Optimal settings by DoE resulted in improvements of 19.4% and 54.7% in the reliability index for a standard mixture and human urine, respectively, as compared with the default setting, and a total of 38 h was required to complete the optimization. Moreover, threshold settings were optimized by using CCD for further improvement. The approach combining optimal parameter setting and the threshold method improved the reliability index about 9.5 times for a standards mixture and 14.5 times for human urine data, which required a total of 41 h. Validation results also showed improvements in the reliability index of about 5–7 times even for urine samples from different subjects. It is concluded that the proposed methodology can be used as a time-saving approach for improving the processing of LC-MS-based metabolomics data

Impact of Dietary Polydextrose Fiber on the Human Gut Metabolome

The aim of the present study was to elucidate the impact of polydextrose PDX an soluble fiber, on the human fecal metabolome by high-resolution nuclear magnetic resonance (NMR) spectroscopy-based metabolomics in a dietary intervention study (<i>n</i> = 12). Principal component analysis (PCA) revealed a strong effect of PDX consumption on the fecal metabolome, which could be mainly ascribed to the presence of undigested fiber and oligosaccharides formed from partial degradation of PDX. Our results demonstrate that NMR-based metabolomics is a useful technique for metabolite profiling of feces and for testing compliance to dietary fiber intake in such trials. In addition, novel associations between PDX and the levels of the fecal metabolites acetate and propionate could be identified. The establishment of a correlation between the fecal metabolome and levels of <i>Bifidobacterium</i> (<i>R</i>² = 0.66) and <i>Bacteroides</i> (<i>R</i>² = 0.46) demonstrates the potential of NMR-based metabolomics to elucidate metabolic activity of bacteria in the gut

Metabolic Fate of ¹³C‑Labeled Polydextrose and Impact on the Gut Microbiome: A Triple-Phase Study in a Colon Simulator

The present study introduces a novel triple-phase (liquids, solids, and gases) approach, which employed uniformly labeled [U–¹³C] polydextrose (PDX) for the selective profiling of metabolites generated from dietary fiber fermentation in an in vitro colon simulator using human fecal inocula. Employing ¹³C NMR spectroscopy, [U–¹³C] PDX metabolism was observed from colonic digest samples. The major ¹³C-labeled metabolites generated were acetate, butyrate, propionate, and valerate. In addition to these short-chain fatty acids (SCFAs), ¹³C-labeled lactate, formate, succinate, and ethanol were detected in the colon simulator samples. Metabolite formation and PDX substrate degradation were examined comprehensively over time (24 and 48 h). Correlation analysis between ¹³C NMR spectra and gas production confirmed the anaerobic fermentation of PDX to SCFAs. In addition, 16S rRNA gene analysis showed that the level of <i>Erysipelotrichaceae</i> was influenced by PDX supplementation and <i>Erysipelotrichaceae</i> level was statistically correlated with SCFA formation. Overall, our study demonstrates a novel approach to link substrate fermentation and microbial function directly in a simulated colonic environment