Qualitative Nitrogen Malnutrition Damages Gut and Alters Microbiome in Adult Mice. A Preliminary Histopathological Study.

Amino-acids (AAs) are the exclusive source of nitrogen for cells. AAs result from the breakdown of food proteins and are absorbed by mucosa of the small intestine that act as a barrier to harmful materials. The quality of food proteins may differ, since it reflects content in Essential-AAs (EAAs) and digestibility but, until now, attention was paid mainly to the interaction between indigested proteins as a whole and microbiota. The link between microbiome and quality of proteins has been poorly studied, although these metabolic interactions are becoming more significant in different illnesses. We studied the effects of a special diet containing unbalanced EAAs/Non-EAAs ratio, providing excess of Non-EAAs, on the histopathology of gut epithelium and on the microbiome in adult mice, as model of qualitative malnutrition. Excess in Non-EAAs have unfavorable quick effect on body weight, gut cells, and microbiome, promoting weakening of the intestinal barrier. Re-feeding these animals with standard diet partially reversed the body alterations. The results prove that an unbalanced EAAs/Non-EAAs ratio is primarily responsible for microbiome modifications, not vice-versa. Therefore, treating microbiota independently by treating co-existing qualitative malnutrition does not make sense. This study also provides a reproducible model of sarcopenia-wasting cachexia like the human protein malnutrition.

Assessing the role of amino acids in systemic inflammation and organ failure in patients with ACLF.

BACKGROUND & AIMS: Systemic inflammation and organ failure(s) are the hallmarks of acute-on-chronic liver failure (ACLF), yet their pathogenesis remains uncertain. Herein, we aimed to assess the role of amino acids in these processes in patients with ACLF. METHODS: The blood metabolomic database of the CANONIC study (comprising 137 metabolites, with 43% related to amino acids) - obtained in 181 patients with ACLF and 650 with acute decompensation without ACLF (AD) - was reanalyzed with a focus on amino acids, in particular 9 modules of co-regulated metabolites. We also compared blood metabolite levels between ACLF and AD. RESULTS: The main findings in ACLF were: i) Metabolite modules were increased in parallel with increased levels of markers of systemic inflammation and oxidative stress. ii) Seventy percent of proteinogenic amino acids were present and most were increased. iii) A metabolic network, comprising the amino acids aspartate, glutamate, the serine-glycine one-carbon metabolism (folate cycle), and methionine cycle, was activated, suggesting increased purine and pyrimidine nucleotide synthesis. iv) Cystathionine, L-cystine, glutamate and pyroglutamate, which are involved in the transsulfuration pathway (a methionine cycle branch) were increased, consistent with increased synthesis of the antioxidant glutathione. v) Intermediates of the catabolism of 5 out of the 6 ketogenic amino acids were increased. vi) The levels of spermidine (a polyamine inducer of autophagy with anti-inflammatory effects) were decreased. CONCLUSIONS: In ACLF, blood amino acids fueled protein and nucleotide synthesis required for the intense systemic inflammatory response. Ketogenic amino acids were extensively catabolized to produce energy substrates in peripheral organs, an effect that was insufficient because organs failed. Finally, the decrease in spermidine levels may cause a defect in autophagy contributing to the proinflammatory phenotype in ACLF. LAY SUMMARY: Systemic inflammation and organ failures are hallmarks of acute-on-chronic liver failure (ACLF). Herein, we aimed to characterize the role of amino acids in these processes. The blood metabolome of patients with acutely decompensated cirrhosis, and particularly those with ACLF, reveals evidence of intense skeletal muscle catabolism. Importantly, amino acids (along with glucose), are used for intense anabolic, energy-consuming metabolism in patients with ACLF, presumably to support de novo nucleotide and protein synthesis in the activated innate immune system.

Revealing Metabolic Perturbation Following Heavy Methamphetamine Abuse by Human Hair Metabolomics and Network Analysis.

Methamphetamine (MA) is a highly addictive central nervous system stimulant. Drug addiction is not a static condition but rather a chronically relapsing disorder. Hair is a valuable and stable specimen for chronic toxicological monitoring as it retains toxicants and metabolites. The primary focus of this study was to discover the metabolic effects encompassing diverse pathological symptoms of MA addiction. Therefore, metabolic alterations were investigated in human hair following heavy MA abuse using both targeted and untargeted mass spectrometry and through integrated network analysis. The statistical analyses (t-test, variable importance on projection score, and receiver-operator characteristic curve) demonstrated that 32 metabolites (in targeted metabolomics) as well as 417 and 224 ion features (in positive and negative ionization modes of untargeted metabolomics, respectively) were critically dysregulated. The network analysis showed that the biosynthesis or metabolism of lipids, such as glycosphingolipids, sphingolipids, glycerophospholipids, and ether lipids, as well as the metabolism of amino acids (glycine, serine and threonine; cysteine and methionine) is affected by heavy MA abuse. These findings reveal crucial metabolic effects caused by MA addiction, with emphasis on the value of human hair as a diagnostic specimen for determining drug addiction, and will aid in identifying robust diagnostic markers and therapeutic targets.

Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes.

A human papillomavirus type plays an important role in the early diagnosis of cervical cancer. Most of the prediction methods use protein sequence and structure information, but the reduced amino acid modes have not been used until now. In this paper, we introduced the modes of reduced amino acids to predict high-risk HPV. We first reduced 20 amino acids into several nonoverlapping groups and calculated their structure and physicochemical modes for high-risk HPV prediction, which was tested and compared with the existing methods on 68 samples of known HPV types. The experiment result indicates that the proposed method achieved better performance with an accuracy of 96.49%, indicating that the reduced amino acid modes might be used to improve the prediction of high-risk HPV types.

Plasma metabolomic profiling of amino acids and polar lipids in Iranian obese adults.

BACKGROUND: Obesity, widely recognized as a serious health concern, is characterized by profoundly altered metabolism. However, the intermediate metabolites involved in this change remain largely unknown. OBJECTIVE: We conducted targeted metabolomics profiling to identify moieties associated with adult obesity. METHODS: In this case-control study of Iranian adults, 200 obese patients were compared with 100 controls based on 104 metabolites profiled by a targeted metabolomic approach using liquid chromatography coupled to triple quadrupole mass spectrometry (LC-MS/MS). The analysis comprised acylcarnitines, diacyl-phosphatidylcholines (PCaa), acyl-alkyl-phosphatidylcholines (PCae), sphingomyelins (SM), lyso-phospholipids (LPC) and amino acids. We performed multivariable linear regression to identify metabolites associated with obesity, adjusting for age, sex, total energy intake, total physical activity, smoking, and alcohol consumption. The Bonferroni correction was used to adjust for multiple testing. RESULTS: A pattern of 19 metabolites was significantly associated with obesity. Branched chain amino acids, alanine, glutamic acid, proline, tyrosine LPCa C16:1, PCaa C32:1, PCaa C32:2 and PCaa C38:3 were positively, while serine, asparagine, LPCa C18:1, LPCa C18:2, LPCe C18:0, PCae C34:3, PCae C38:4 and PCae C40:6 were negatively associated with obesity (all p < 0.00048). CONCLUSIONS: A metabolomic profile containing 9 amino acids and 10 polar lipids may serve as a potential biomarker of adult obesity. Further studies are warranted to replicate these findings as well as investigate potential changes in this profile after weight reduction.

Alterations in serum amino-acid profile in the progression of colorectal cancer: associations with systemic inflammation, tumour stage and patient survival.

BACKGROUND: Cancer cachexia is a complex wasting syndrome affecting patients with advanced cancer, with systemic inflammation as a key component in pathogenesis. Protein degradation and release of amino acids (AAs) in skeletal muscle are stimulated in cachexia. Here, we define factors contributing to serum AA levels in colorectal cancer (CRC). METHODS: Serum levels of nine AAs were characterised in 336 CRC patients and their relationships with 20 markers of systemic inflammatory reaction, clinicopathological features of cancers and patient survival were analysed. RESULTS: Low serum glutamine and histidine levels and high phenylalanine levels associated with indicators of systemic inflammation, including high modified Glasgow Prognostic Score, high blood neutrophil/lymphocyte ratio and high serum levels of CRP, IL-6 and IL-8. Low levels of serum glutamine, histidine, alanine and high glycine levels also associated with advanced cancer stage and with poor cancer-specific survival in univariate analysis. CONCLUSIONS: In CRC, serum AA levels are associated with systemic inflammation and disease stage. These findings may reflect muscle catabolism induced by systemic inflammation in CRC.

Amino Acid Transport Across the Mammalian Intestine.

The small intestine mediates the absorption of amino acids after ingestion of protein and sustains the supply of amino acids to all tissues. The small intestine is an important contributor to plasma amino acid homeostasis, while amino acid transport in the large intestine is more relevant for bacterial metabolites and fluid secretion. A number of rare inherited disorders have contributed to the identification of amino acid transporters in epithelial cells of the small intestine, in particular cystinuria, lysinuric protein intolerance, Hartnup disorder, iminoglycinuria, and dicarboxylic aminoaciduria. These are most readily detected by analysis of urine amino acids, but typically also affect intestinal transport. The genes underlying these disorders have all been identified. The remaining transporters were identified through molecular cloning techniques to the extent that a comprehensive portrait of functional cooperation among transporters of intestinal epithelial cells is now available for both the basolateral and apical membranes. Mouse models of most intestinal transporters illustrate their contribution to amino acid homeostasis and systemic physiology. Intestinal amino acid transport activities can vary between species, but these can now be explained as differences of amino acid transporter distribution along the intestine. © 2019 American Physiological Society. Compr Physiol 9:343-373, 2019.

Helical Structures of Cyclopentene-based α,α-Disubstituted α-Amino Acid Homopeptides.

The cyclopentene-based α,α-disubstituted α-amino acid Ac5c= and its homopeptides, up to nonapeptides, were synthesized. The side-chain cyclopentene was expected to become symmetric, the Cα-carbon to be puckered, and other Cß, Cß', Cγ, Cγ'-carbons to be coplanar. As expected, side-chain cyclopentene conformations became symmetric and Cα-carbons were puckered. Conformational studies using FT-IR absorption, 1H NMR spectra, and X-ray crystallographic analyses revealed that Ac5c= homopeptides did not form a planar conformation, but assumed a 310-helical structure, similar to cyclopentane-based α,α-disupstituted α-amino acid homopeptides.

Comparison of Free Total Amino Acid Compositions and Their Functional Classifications in 13 Wild Edible Mushrooms.

Thirteen popular wild edible mushroom species in Yunnan Province, Boletus bicolor, Boletus speciosus, Boletus sinicus, Boletus craspedius, Boletus griseus, Boletus ornatipes, Xerocomus, Suillus placidus, Boletinus pinetorus, Tricholoma terreum, Tricholomopsis lividipileata, Termitomyces microcarpus, and Amanita hemibapha, were analyzed for their free amino acid compositions by online pre-column derivazation reversed phase high-performance liquid chromatography (RP-HPLC) analysis. Twenty free amino acids, aspartic acid, glutamic acid, serine, glycine, alanine, praline, cysteine, valine, methionine, phenylalanine, isoleucine, leucine, lysine, histidine, threonine, asparagines, glutamine, arginine, tyrosine, and tryptophan, were determined. The total free amino acid (TAA) contents ranged from 1462.6 mg/100 g in B. craspedius to 13,106.2 mg/100 g in T. microcarpus. The different species showed distinct free amino acid profiles. The ratio of total essential amino acids (EAA) to TAA was 0.13-0.41. All of the analyzed species showed high contents of hydrophobic amino acids, at 33%-54% of TAA. Alanine, cysteine, glutamine, and glutamic acid were among the most abundant amino acids present in all species. The results showed that the analyzed mushrooms possessed significant free amino acid contents, which may be important compounds contributing to the typical mushroom taste, nutritional value, and potent antioxidant properties of these wild edible mushrooms. Furthermore, the principal component analysis (PCA) showed that the accumulative variance contribution rate of the first four principal components reached 94.39%. Cluster analysis revealed EAA composition and content might be an important parameter to separate the mushroom species, and T. microcarpus and A. hemibapha showed remarkable EAA content among the 13 species.

Nonvolatile Taste Components and Antioxidant Properties of Fruiting Body and Mycelium with High Ergothioneine Content from the Culinary-Medicinal Golden Oyster Mushroom Pleurotus citrinopileatus (Agaricomycetes).

Pleurotus citrinopileatus mycelium was prepared with high ergothioneine (Hi-Ergo) content and its proximate composition, nonvolatile taste components, and antioxidant properties were studied. The ergothioneine contents of fruiting bodies and Hi-Ergo and regular mycelia were 3.89, 14.57, and 0.37 mg/g dry weight, respectively. Hi-Ergo mycelium contained more dietary fiber, soluble polysaccharides, and ash but less carbohydrates, reducing sugar, fiber, and fat than regular mycelium. However, Hi-Ergo mycelium contained the smallest amounts of total sugars and polyols (47.43 mg/g dry weight). In addition, Hi-Ergo mycelium showed the most intense umami taste. On the basis of the half-maximal effective concentration values obtained, the 70% ethanolic extract from Hi-Ergo mycelium showed the most effective antioxidant activity, reducing power, and scavenging ability, whereas the fruiting body showed the most effective antioxidant activity, chelating ability, and Trolox-equivalent antioxidant capacity. Overall, Hi-Ergo mycelium could be beneficially used as a food-flavoring material or as a nutritional supplement.

50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification

BACKGROUND: Physicochemical properties are frequently analyzed to characterize protein-sequences of known and unknown function. Especially the hydrophobicity of amino acids is often used for structural prediction or for the detection of membrane associated or embedded ß-sheets and α-helices. For this purpose many scales classifying amino acids according to their physicochemical properties have been defined over the past decades. In parallel, several hydrophobicity parameters have been defined for calculation of peptide properties. We analyzed the performance of separating sequence pools using 98 hydrophobicity scales and five different hydrophobicity parameters, namely the overall hydrophobicity, the hydrophobic moment for detection of the α-helical and ß-sheet membrane segments, the alternating hydrophobicity and the exact ß-strand score. RESULTS: Most of the scales are capable of discriminating between transmembrane α-helices and transmembrane ß-sheets, but assignment of peptides to pools of soluble peptides of different secondary structures is not achieved at the same quality. The separation capacity as measure of the discrimination between different structural elements is best by using the five different hydrophobicity parameters, but addition of the alternating hydrophobicity does not provide a large benefit. An in silico evolutionary approach shows that scales have limitation in separation capacity with a maximal threshold of 0.6 in general. We observed that scales derived from the evolutionary approach performed best in separating the different peptide pools when values for arginine and tyrosine were largely distinct from the value of glutamate. Finally, the separation of secondary structure pools via hydrophobicity can be supported by specific detectable patterns of four amino acids. CONCLUSION: It could be assumed that the quality of separation capacity of a certain scale depends on the spacing of the hydrophobicity value of certain amino acids. Irrespective of the wealth of hydrophobicity scales a scale separating all different kinds of secondary structures or between soluble and transmembrane peptides does not exist reflecting that properties other than hydrophobicity affect secondary structure formation as well. Nevertheless, application of hydrophobicity scales allows distinguishing between peptides with transmembrane α-helices and ß-sheets. Furthermore, the overall separation capacity score of 0.6 using different hydrophobicity parameters could be assisted by pattern search on the protein sequence level for specific peptides with a length of four amino acids.

Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.

[Classification of amino acids based on a comparative analysis of contacts in DNA-protein complexes and specific DNA-protein interactions].

The classification of amino acid residues based on the events of contact formation between distinct amino acid and selected nucleotides was constructed. Thus, the most integral properties, that characterize interactions in organization of DNA-protein complexes, were used. We applied the Voronoi-Delaunay tessellation to draw statistics of contacts and area of contacts for the set included 1937 DNA-protein complexes. Similarities of amino acid residues have been searched for based on the comparison of corresponded rows and matrixes of contacts and areas of contacts. Nine measures of distance were used for estimation of rows similarity degree. The procedure of clustering amino acids in groups included three hierarchical and two nonhierarchical methods. A total tree was built using nine techniques of estimating distance with three hierarchical clustering methods. It was shown that clustering centers in the main groups are always constant while other relationships between objects vary. Clustering of binary associations was found for the most amino acids. Major classes of up to six amino acids correspond to the certain local structures of the polypeptide chain in the context of amino acid composition. These data should be taken into account when designing DNA-protein ligands.

Colored petri nets to model gene mutation and amino acids classification.

The genetic code is the triplet code based on the three-letter codons, which determines the specific amino acid sequences in proteins synthesis. Choosing an appropriate model for processing these codons is a useful method to study genetic processes in Molecular Biology. As an effective modeling tool of discrete event dynamic systems (DEDS), colored petri net (CPN) has been used for modeling several biological systems, such as metabolic pathways and genetic regulatory networks. According to the genetic code table, CPN is employed to model the process of genetic information transmission. In this paper, we propose a CPN model of amino acids classification, and further present the improved CPN model. Based on the model mentioned above, we give another CPN model to classify the type of gene mutations via contrasting the bases of DNA strands and the codons of amino acids along the polypeptide chain. This model is helpful in determining whether a certain gene mutation will cause the changes of the structures and functions of protein molecules. The effectiveness and accuracy of the presented model are illustrated by the examples in this paper.

Olfactory sensitivity to amino acids in the blackspot sea bream (Pagellus bogaraveo): a comparison between olfactory receptor recording techniques in seawater.

The current study investigated the olfactory sensitivity of the blackspot sea bream to amino acids, odorants associated with food detection in fish, and compared the efficacy of two different experimental methods: multi-unit recording from the olfactory nerve and the electro-olfactogram (EOG). Twenty essential amino acids plus L-DOPA evoked clear, concentration-dependent olfactory responses using both methods, with estimated thresholds of 10(-8.5)-10(-6.2) M (nerve recording) and 10(-7.5)-10(-4.8) M (EOG). The most potent amino acids were L-cysteine, L-methionine (both sulphur-containing), L-alanine, L-leucine (both neutral), L-glutamine (amide-containing) and L-serine (hydroxyl-containing). The least potent were L-proline (secondary α-amino group), the aromatic amino acids and glycine (simplest). Although the rank order of olfactory potency was similar for the two methods used, and the calculated thresholds given by the two methods were positively correlated, the sensitivity of the EOG was consistently lower than multi-unit recording by approximately one order of magnitude, presumably due to the electrical shunting effect of seawater. As in freshwater, the EOG could be a valid method for comparing olfactory potency of different odorants in stenohaline marine fish; however, for absolute 'biological' thresholds, a more invasive recording technique, such as multi-unit recording from the olfactory nerve, should be used.

Toward quantitative characterization of the binding profile between the human amphiphysin-1 SH3 domain and its peptide ligands.

A new structure-based approach was proposed to quantitatively characterize the binding profile of human amphiphysin-1 (hAmph1) SH3 domain-peptide complexes. In this protocol, the protein/peptide atoms were classified into 16 types in terms of their physicochemical meaning and biological function, and then a 16 x 16 atom-pair interaction matrix was constructed to describe 256 atom-pair types between the SH3 domain and the peptide ligand, with atoms from peptide and SH3 domain served as the matrix columns and rows, respectively. Three non-covalent effects dominating SH3 domain-peptide binding as electrostatic, van der Waals (steric) and hydrophobic interactions were separately calculated for the 256 atom-pair types. As a result, 768 descriptors coding detailed information about SH3 domain-peptide interactions were yielded for further statistical modeling and analysis. Based on a culled data set consisting of 592 samples with known affinities, we employed this approach, coupled with partial least square (PLS) regression and genetic algorithm (GA), to predict and to interpret the peptide-binding behavior to SH3 domain. In comparison with the previous works, our method is more capable of capturing important factors in the SH3 domain-peptide binding, thus, yielding models with better statistical performance. Furthermore, the optimal GA/PLS model indicates that the electrostatic effect plays a crucial role in SH3 domain-peptide complexes, and steric contact and hydrophobic force also contribute significantly to the binding.

Amino acids: metabolism, functions, and nutrition.

Recent years have witnessed the discovery that amino acids (AA) are not only cell signaling molecules but are also regulators of gene expression and the protein phosphorylation cascade. Additionally, AA are key precursors for syntheses of hormones and low-molecular weight nitrogenous substances with each having enormous biological importance. Physiological concentrations of AA and their metabolites (e.g., nitric oxide, polyamines, glutathione, taurine, thyroid hormones, and serotonin) are required for the functions. However, elevated levels of AA and their products (e.g., ammonia, homocysteine, and asymmetric dimethylarginine) are pathogenic factors for neurological disorders, oxidative stress, and cardiovascular disease. Thus, an optimal balance among AA in the diet and circulation is crucial for whole body homeostasis. There is growing recognition that besides their role as building blocks of proteins and polypeptides, some AA regulate key metabolic pathways that are necessary for maintenance, growth, reproduction, and immunity. They are called functional AA, which include arginine, cysteine, glutamine, leucine, proline, and tryptophan. Dietary supplementation with one or a mixture of these AA may be beneficial for (1) ameliorating health problems at various stages of the life cycle (e.g., fetal growth restriction, neonatal morbidity and mortality, weaning-associated intestinal dysfunction and wasting syndrome, obesity, diabetes, cardiovascular disease, the metabolic syndrome, and infertility); (2) optimizing efficiency of metabolic transformations to enhance muscle growth, milk production, egg and meat quality and athletic performance, while preventing excess fat deposition and reducing adiposity. Thus, AA have important functions in both nutrition and health.

Modeling multi-typed structurally viewed chemicals with the UMLS Refined Semantic Network.

OBJECTIVE: Chemical concepts assigned multiple "Chemical Viewed Structurally" semantic types (STs) in the Unified Medical Language System (UMLS) are subject to ambiguous interpretation. The multiple assignments may denote the fact that a specific represented chemical (combination) is a conjugate, derived via a chemical reaction of chemicals of the different types, or a complex, composed of a mixture of such chemicals. The previously introduced Refined Semantic Network (RSN) is modified to properly model these varied multi-typed chemical combinations. DESIGN: The RSN was previously introduced as an enhanced abstraction of the UMLS's concepts. It features new types, called intersection semantic types (ISTs), each of which explicitly captures a unique combination of ST assignments in one abstract unit. The ambiguous ISTs of different "Chemical Viewed Structurally" ISTs of the RSN are replaced with two varieties of new types, called conjugate types and complex types, which explicitly denote the nature of the chemical interactions. Additional semantic relationships help further refine that new portion of the RSN rooted at the ST "Chemical Viewed Structurally." MEASUREMENTS: The number of new conjugate and complex types and the amount of changes to the type assignment of chemical concepts are presented. RESULTS: The modified RSN, consisting of 35 types and featuring 22 new conjugate and complex types, is presented. A total of 800 (about 98%) chemical concepts representing multi-typed chemical combinations from "Chemical Viewed Structurally" STs are uniquely assigned one of the new types. An additional benefit is the identification of a number of illegal ISTs and ST assignment errors, some of which are direct violations of exclusion rules defined by the UMLS Semantic Network. CONCLUSION: The modified RSN provides an enhanced abstract view of the UMLS's chemical content. Its array of conjugate and complex types provides a more accurate model of the variety of combinations involving chemicals viewed structurally. This framework will help streamline the process of type assignments for such chemical concepts and improve user orientation to the richness of the chemical content of the UMLS.

Cross-species comparison of metabolite profiles in chemosensory epithelia: an indication of metabolite roles in chemosensory cells.

Comparative studies of chemosensory systems in vertebrates and invertebrates have greatly enhanced our understanding of anatomical and physiological constraints of chemical detection. Immunohistochemical comparisons of chemosensory systems are difficult to make across species due to limited cross-reactivity of mammalian-based antibodies. Immunostaining chemosensory tissues with glutaraldehyde-based antibodies generated against small metabolites in combination with hierarchical cluster analyses provide a novel approach for identifying and classifying cell types regardless of species. We used this "metabolite profiling" technique to determine whether metabolite profiles can be used to identify cell classes within and across different species including mouse, zebrafish, lobster and squid. Within a species, metabolite profiles for distinct cell classes were generally consistent. We found several metabolite-based cell classifications that mirrored function or receptor protein-based classifications. Although profiles of all six metabolites differed across species, we found that specific metabolites were associated with certain cell types. For example, elevated levels of glutathione were characteristic of nonsensory cells from vertebrates, suggesting an antioxidative role in non-neuronal cells in sensory tissues. Collectively, we found significantly different metabolite profiles for distinct cell populations in chemosensory tissue within all of the species studied. Based on their roles in other systems or cells, we discuss the roles of L-arginine, L-aspartate, L-glutamate, glycine, glutathione, and taurine within chemosensory epithelia.

Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13C NMR.

We report a chemical derivatization method that selects a class of metabolites from a complex mixture and enhances their detection by 13C NMR. Acetylation of amines directly in aqueous medium with 1,1'-13C(2) acetic anhydride is a simple method that creates a high sensitivity and quantitative label in complex biofluids with minimal sample pretreatment. Detection using either 1D or 2D 13C NMR experiments produces highly resolved spectra with improved sensitivity. Experiments to identify and compare amino acids and related metabolites in normal human urine and serum samples as well as in urine from patients with the inborn errors of metabolism tyrosinemia type II, argininosuccinic aciduria, homocystinuria, and phenylketonuria demonstrate the method. The use of metabolite derivatization and 13C NMR spectroscopy produces data suitable for metabolite profiling analysis of biofluids on a time scale that allows routine use. Extension of this approach to enhance the NMR detection of other classes of metabolites has also been accomplished. The improved detection of low-concentration metabolites shown here creates opportunities to improve the understanding of the biological processes and develop improved disease detection methodologies.