Bitter Odorants and Odorous Bitters: Toxicity and Human TAS2R Targets

Membrane-bound chemoreception of bitter bile acids and peptides is mediated by the same subset of bitter taste receptors

The vertebrate sense of taste allows rapid assessment of the nutritional quality and potential presence of harmful substances prior to ingestion. Among the five basic taste qualities, salty, sour, sweet, umami, and bitter, bitterness is associated with the presence of putative toxic substances and elicits rejection behaviors in a wide range of animals including humans. However, not all bitter substances are harmful, some are thought to be health-beneficial and nutritious. Among those compound classes that elicit a bitter taste although being non-toxic and partly even essential for humans are bitter peptides and L-amino acids. Using functional heterologous expression assays, we observed that the 5 dominant human bitter taste receptors responsive to bitter peptides and amino acids are activated by bile acids, which are notorious for their extreme bitterness. We further demonstrate that the cross-reactivity of bitter taste receptors for these two different compound classes is evolutionary conserved and can be traced back to the amphibian lineage. Moreover, we show that the cross-detection by some receptors relies on "structural mimicry" between the very bitter peptide L-Trp-Trp-Trp and bile acids, whereas other receptors exhibit a phylogenetic conservation of this trait. As some bile acid-sensitive bitter taste receptor genes fulfill dual-roles in gustatory and non-gustatory systems, we suggest that the phylogenetic conservation of the rather surprising cross-detection of the two substance classes could rely on a gene-sharing-like mechanism in which the non-gustatory function accounts for the bitter taste response to amino acids and peptides.

BitterMasS: Predicting Bitterness from Mass Spectra

Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.

Can humans smell tastants?

Although studies have shown that olfaction may contribute to the perception of tastant, literature is scarce or circumstantial, especially in humans. This study aims to (i) explore whether humans can perceive solutions of basic prototypical tastants through orthonasal and retronasal olfaction and (ii) to examine what volatile odor compounds (VOCs) underlie this ability. Solutions of 5 basic tastants (sucrose, sodium chloride, citric acid, monosodium glutamate [MSG], quinine) dissolved in water, and 2 fatty acids (oleic and linoleic acid) dissolved in mineral oil were prepared. Triangle discrimination tests were performed (n = 41 in duplicate) to assess whether the tastant solutions can be distinguished from blanks (solvents) through ortho- and retronasal olfaction. Participants were able to distinguish all tastant solutions from blank through orthonasal olfaction. Only sucrose, sodium chloride, oleic acid, and linoleic acid were distinguished from blank by retronasal olfaction. Ethyl dichloroacetate, methylene chloride, and/or acetone were identified in the headspace of sucrose, MSG, and quinine solutions but not in the headspace of water, sodium chloride, and citric acid solutions. Fat oxidation compounds such as alcohols and aldehydes were detected in the headspace of the oleic and linoleic acid solutions but not the mineral oil. We conclude that prototypical tastant solutions can be discriminated from water and fatty acid solutions from mineral oil through orthonasal olfaction. Differences in the volatile headspace composition between blanks and tastant solutions may have facilitated the olfactory discrimination. These findings can have methodological implications for future studies assessing gustatory perception using these prototypical taste compounds.

Abatement of odor emissions from wastewater treatment plants using biochar

Odor is a critical environmental problem that negatively affects people's quality of life. Wastewater treatment plants (WWTPs) often emit various odorous compounds, such as ammonia, sulfur dioxide, and organosulfur. Abatement of odor emissions from WWTPs using biochar may contribute to achieving carbon neutrality due to the carbon negative nature, CO2 sorption, and negative priming effects of biochar. Biochar has a high specific surface area and microporous structure with appropriate activation, which is suitable for sorption purposes. Various research directions have been proposed to determine the biochar removal efficiency for different odorants released from WWTPs. According to the literature survey, the pre- and post-treatments (e.g., thermal treatment, chemical treatment, and metal impregnation) of biochar could enhance the removal capacity for the odorants emitted from WWTPs at comparable conditions, compared to unmodified biochar. The feedstock and production condition (particularly, pyrolysis temperature) of a biochar and initial concentration of an odorant markedly affect the biochar's odorant removal capacity and efficiency. Moreover, different adsorption systems for the removal of odorants emitted from WWTPs follow different adsorption models. Further research is required to establish the practical use of biochar for the mitigation of odors released from WWTPs.

Predicting Antioxidant Synergism via Artificial Intelligence and Benchtop Data

Lipid oxidation is a major issue affecting products containing unsaturated fatty acids as ingredients or components, leading to the formation of low molecular weight species with diverse functional groups that impart off-odors and off-flavors. Aiming to control this process, antioxidants are commonly added to these products, often deployed as combinations of two or more compounds, a strategy that allows for lowering the amount used while boosting the total antioxidant capacity of the formulation. While this approach allows for minimizing the potential organoleptic and toxic effects of these compounds, predicting how these mixtures of antioxidants will behave has traditionally been one of the most challenging tasks, often leading to simple additive, antagonistic, or synergistic effects. Approaches to understanding these interactions have been predominantly empirically driven but thus far, inefficient and unable to account for the complexity and multifaceted nature of antioxidant responses. To address this current gap in knowledge, we describe the use of an artificial intelligence model based on deep learning architecture to predict the type of interaction (synergistic, additive, and antagonistic) of antioxidant combinations. Here, each mixture was associated with a combination index value (CI) and used as input for our model, which was challenged against a test (n = 140) data set. Despite the encouraging preliminary results, this algorithm failed to provide accurate predictions of oxidation experiments performed in-house using binary mixtures of phenolic antioxidants and a lard sample. To overcome this problem, the AI algorithm was then enhanced with various amounts of experimental data (antioxidant power data assessed by the TBARS assay), demonstrating the importance of having chemically relevant experimental data to enhance the model's performance and provide suitable predictions with statistical relevance. We believe the proposed method could be used as an auxiliary tool in benchmark analysis routines, offering a novel strategy to enable broader and more rational predictions related to the behavior of antioxidant mixtures.

Reduction of Odor-causing Compounds in Wastewater using Biochar: A Review

Wastewater contains chemical compounds that cause malodors, such as ammonium cation, dimethyl sulfide, and volatile organic compounds. Biochar-based reduction in the odorants has been proposed as an effective approach along with maintaining environmental neutrality as biochar is a sustainable material made from biomass and biowaste. Biochar can have high specific surface area and microporous structure with proper activation, appropriate for sorption purposes. Recently, various research directions have been proposed to determine the removal efficiency of biochar for different odorants contained in wastewater. This article is aimed at providing the most updated review of biochar-based removal of odor-causing compounds in wastewater while highlighting the current advances. It was distinguished that the odorant removal performance of biochar is highly associated with the raw material and modification method of biochar, and the kind of odorants. Further research should be required for more practical use of biochar for the reduction of odorants in wastewater.