Plasma amino acid analysis for diagnosis and amino acid-based metabolic networks

Optimization of ovum pick-up-in vitro fertilization and in vitro growth of immature oocytes in ruminants

Due to the strong demand for embryo production from young and genotyped superior animals using ovum-pick up (OPU) combined with in vitro fertilization (IVF), the number of in vitro-produced embryos has exceeded that of in vivo-derived embryos globally since 2016. One of the merits of OPU-IVF is that the administration of follicle-stimulating hormone (FSH) is not essential, while FSH treatment prior to OPU promotes oocyte developmental competence. Thus, investigations are needed to optimize OPU-IVF protocols with and without FSH. In addition, OPU enables oocyte collection from antral follicles in living animals. However, there are numerous immature oocytes in follicles at earlier stages, which are potentially destined to degenerate in ovaries. The technology used to foster acquisition of maturational and developmental competences in these immature oocytes is called in vitro growth (IVG). IVG is expected to contribute to assisted reproductive technologies for livestock, humans, and endangered species. However, no offspring from preantral follicles has been reported using IVG in animals other than in mice. Furthermore, IVG can be used to investigate factors affecting the fertility and developmental competence of oocytes by reconstituting follicle growth at each stage in vitro, which cannot be evaluated in vivo. Here, the technological progress of the optimization of immature bovine oocyte utilization is reviewed alongside findings from a variety of other ruminants.

Plasma-free Amino Acid Profile is Beneficial for Breast Cancer Screening in Women With Dense Breasts

BACKGROUND

AminoIndex™ Cancer Screening (AICS breast) was developed as a breast cancer screening test using multivariate analysis of plasma free amino acid (PFAA) profiles. This study investigated the relationship between the AICS breast rank and breast density on mammography (MMG) for the detection of breast cancer.

MATERIALS AND METHODS

MMG and blood samples were obtained preoperatively from 224 patients with breast cancer who did not receive neoadjuvant chemotherapy between 2017 and 2019. PFAA concentration was measured using liquid chromatography-mass spectrometry, and the AICS breast and AICS ranks were calculated. Detection rates were compared between MMG (categories 3-5) and AICS breasts (ranks B and C) according to breast density.

RESULTS

Breast density was classified as extremely dense in 9.4%, heterogeneously dense in 48.2%, scattered in 29.9%, and fatty in 12.5% of patients. Dense breasts (extremely dense and heterogeneously dense) represented 57.6%. The overall detection rate by MMG was 82.6% and significantly lower in patients with dense breasts (74.4%) compared to non-dense breasts (93.7%). The overall detection rate by AICS breast was 50.0%, with no difference between patients with dense breasts (45.7%) and those with non-dense breasts (55.8%). The combination of MMG and AICS breast increased the detection rate to 91.5% overall, 88.3% in patients with dense breasts, and 95.8% in those with non-dense breasts.

CONCLUSION

This study demonstrated that the detection rate of AICS breast was not associated with breast density, unlike MMG. Adding AICS breast to MMG may be beneficial for breast cancer screening in patients with dense breasts.

Aging-Related Sarcopenia: Metabolic Characteristics and Therapeutic Strategies

The proportion of the elderly population is gradually increasing as a result of medical care advances, leading to a subsequent surge in geriatric diseases that significantly impact quality of life and pose a substantial healthcare burden. Sarcopenia, characterized by age-related decline in skeletal muscle mass and quality, affects a considerable portion of older adults, particularly the elderly, and can result in adverse outcomes such as frailty, fractures, bedridden, hospitalization, and even mortality. Skeletal muscle aging is accompanied by underlying metabolic changes. Therefore, elucidating these metabolic profiles and specific mechanisms holds promise for informing prevention and treatment strategies for sarcopenia. This review provides a comprehensive overview of the key metabolites identified in current clinical studies on sarcopenia and their potential pathophysiological alterations in metabolic activity. Besides, we examine potential therapeutic strategies for sarcopenia from a perspective focused on metabolic regulation.

Metabolomics signature of cardiovascular disease in patients with diabetes, a narrative review

Objectives

The exact underlying mechanism of developing diabetes-related cardiovascular disease (CVD) among patients with type 2 diabetes (T2D) is not clear. Metabolomics can provide a platform enabling the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The aim of this review is to summarize the available evidence on the relationship between metabolomics and cardiovascular diseases in patients with diabetes.

Methods

The literature was searched to find out studies that have investigated the relationship between the alteration of specific metabolites and cardiovascular diseases in patients with diabetes.

Results

Evidence proposed that changes in the metabolism of certain amino acids, lipids, and carbohydrates, independent of traditional CVD risk factors, are associated with increased CVD risk.

Conclusions

Metabolomics can provide a platform to enable the prediction, diagnosis, and understanding of the risk of CVD in patients with diabetes mellitus. The association of the alteration in specific metabolites with CVD may be considered in the investigations for the development of new therapeutic targets for the prevention of CVD in patients with diabetes mellitus.

The diagnostic significance of circulating miRNAs and metabolite profiling in early prediction of breast cancer in Egyptian women

OBJECTIVE

Breast cancer (BC) is one of the most commonly diagnosed solid malignancies in women worldwide.

PURPOSE

Finding new non-invasive circulating diagnostic biomarkers will facilitate the early prediction of BC and provide valuable insight into disease progression and response to therapy using a safe and more accessible approach available every inspection time. Therefore, our present study aimed to investigate expression patterns of potentially circulating biomarkers that can differentiate well between benign, malignant, and healthy subjects.

METHODS

To achieve our target, quantitative analyses were performed for some circulating biomarkers which have a role in the proliferation and tumor growth, as well as, glutamic acid, and human epidermal growth receptor 2 (HER2) in blood samples of BC patients in comparison to healthy controls using qRT-PCR, liquid chromatography/mass spectrometry (LC/MS/MS), and ELISA.

RESULTS

Our findings showed that the two miRNAs (miRNA-145, miRNA-382) were expressed at lower levels in BC sera than healthy control group, while miRNA-21 was expressed at higher levels in BC patients than control subjects. Area under ROC curves of BC samples revealed that AUC of miRNA-145, miRNA-382, miRNA-21, and glutamic acid was evaluated to equal 0.99, 1.00, 1.00 and 1.00, respectively. Besides, there was a significantly positive correlation between miRNA-145 and miRNA-382 (r = 0.737), and a highly significant positive correlation between miRNA-21 and glutamic acid (r = 0.385).

CONCLUSION

Based on our results, we conclude that the detection of serum miRNA-145, -382 and -21 as a panel along with glutamic acid, and circulating HER2 concentrations could be useful as a non-invasive diagnostic profiling for early prediction of breast cancer in Egyptian patients. It can provide an insight into disease progression, discriminate between malignancy and healthy control, and overcome the use limitations (low sensitivity and specificity, repeated risky exposure, and high cost) of other detecting tools, including mammography, magnetic resonance imaging, and ultrasound.

Relationship between Amino Acid Metabolism and Bovine In Vitro Follicle Activation and Growth

The amino acid metabolism of bovine follicles during in vitro growth (IVG) was evaluated to identify potential indicators of health during culture. The bovine ovarian cortex was sliced, prepared as strips, and cultured for 6 days. Tissue samples were examined histologically before and after 6 days of culture, and the degree of follicle activation was classified as either high or low based on the number of growing secondary follicles present (high: 7~11; low: 0~1). In a separate experiment, secondary follicles (diameter range: 100~200 μm) were manually isolated and cultured, and their growth was monitored for 6 days. Cultured follicles were classified as growth or degenerate based on diameter change during culture (growth: +60.5~74.1 μm; degenerate: -28~15.2 μm). Free amino acids and their metabolites were measured in the spent culture medium from each group. In cultured ovarian cortical strips, the concentration of α-aminoadipic acid was significantly higher in the low activation group than in the high group (p < 0.05), while those of methionine, lysine, and arginine were higher in the high activation group. In cultured isolated secondary follicles, concentrations of methionine, tyrosine, histidine, and hydroxyproline were higher in the degenerate group (p ≤ 0.05). In conclusion, amino acid metabolism has the potential to serve as an indicator of primordial follicle activation and subsequent growth rate during bovine IVG.

Hematological Changes in Sika Doe and Suckling Fawn Fed with Spent Mushroom Substrate of Pleurotus ostreatus

Sika deer velvet antler is the most important animal nutraceutic in traditional Chinese medicine. Reducing the breeding cost of sika deer by looking for a low-cost diet is the main research direction at present. The purpose of this experiment was to find an alternative diet for sika deer and reduce the cost of the diet by using spent mushroom substrate (SMS) as a concentrate supplement. The apparent digestibility for sika doe and the hematological changes of sika doe and suckling fawn were measured by replacing 10% of the concentrate supplement with SMS of Pleurotus ostreatus (SMS-MP). Compared with the control group, the digestibility of dry matter (DM), total protein (TP), globulin (GLO), and cholesterol (CHOL) of sika doe were significantly decreased (p < 0.05), and glucose (GLU), alanine (Ala), phenylalanine (Phe), and proline (Pro) of sika doe were significantly increased (p < 0.05) after the replacement of SMS-MP. Compared with the control group, the serum GLU of suckling fawn was significantly decreased (p < 0.05) and the phosphatase (ALP) was significantly increased after the replacement of SMS-MP (p < 0.05). There were no significant differences in the immune globulin and amino acid of suckling fawns between the two groups (p > 0.05). The present findings confirm the applicability of SMS-MP as a sika doe concentrate supplement. At the same time, using SMS, a waste resource, can not only reduce the breeding cost of sika doe, but also make full use of SMS to reduce environmental pollution.

Using compositional mixed-effects models to evaluate responses to amino acid supplementation in milk replacers for calves

The consequences of supplementing Lys, Met, and Thr in milk replacers (MR) for calves have been widely studied, but scarce information exists about potential roles of other AA (whether essential or not). The effects on growth performance of supplementation of 4 different AA combinations in a mixed ration (25.4% crude protein and 20.3% fat) based on skim milk powder and whey protein concentrate were evaluated in 76 Holstein male calves (3 ± 1.7 d old). The 4 MR were as follows: CTRL with no AA supplementation; PG, supplying additional 0.3% Pro and 0.1% Gly; FY, supplying additional 0.2% Phe and 0.2% Tyr; and KMT, providing additional 0.62% Lys, 0.22% Met, and 0.61% Thr. All calves were fed the same milk allowance program and were weaned at 56 d of study. Concentrate intake was limited to minimize interference of potential differences in solid feed intake among treatments. Animals were weighed weekly, intakes recorded daily, and blood samples obtained at 2, 5, and 7 wk of study to determine serum urea and plasma AA concentrations. Plasma AA concentrations were explored using compositional data analysis, and their isometric log-ratio transformations were used to analyze their potential influence on ADG and serum urea concentration using a linear mixed-effects model. We detected no differences in calf performance and feed intake. Plasma relative concentration of the AA supplemented in the KMT and PG treatments increased in their respective treatments, and, in PG calves, a slight increase in the proportion of plasma Gly, Glu, and branched-chain AA was also observed. The proportions of plasma branched-chain AA, His, and Gln increased, and those of Thr, Arg, Lys, and Glu decreased with calves' age. A specific log-contrast balance formed by Arg, Thr, and Lys was found to be the main driver for lowering serum urea concentrations and increasing calf growth. The use of compositional mixed-effects models identified a cluster formed by the combination of Arg, Thr, and Lys, as a potential AA to optimize calf growth.

Compartmentalizing Cell-Free Systems: Toward Creating Life-Like Artificial Cells and Beyond

Building an artificial cell is a research area that is rigorously studied in the field of synthetic biology. It has brought about much attention with the aim of ultimately constructing a natural cell-like structure. In particular, with the more mature cell-free platforms and various compartmentalization methods becoming available, achieving this aim seems not far away. In this review, we discuss the various types of artificial cells capable of hosting several cellular functions. Different compartmental boundaries and the mature and evolving technologies that are used for compartmentalization are examined, and exciting recent advances that overcome or have the potential to address current challenges are discussed. Ultimately, we show how compartmentalization and cell-free systems have, and will, come together to fulfill the goal to assemble a fully synthetic cell that displays functionality and complexity as advanced as that in nature. The development of such artificial cell systems will offer insight into the fundamental study of evolutionary biology and the sea of applications as a result. Although several challenges remain, emerging technologies such as artificial intelligence also appear to help pave the way to address them and achieve the ultimate goal.

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.

Regulation of Skeletal Muscle Function by Amino Acids

Amino acids are components of proteins that also exist free-form in the body; their functions can be divided into (1) nutritional, (2) sensory, and (3) biological regulatory roles. The skeletal muscle, which is the largest organ in the human body, representing ~40% of the total body weight, plays important roles in exercise, energy expenditure, and glucose/amino acid usage—processes that are modulated by various amino acids and their metabolites. In this review, we address the metabolism and function of amino acids in the skeletal muscle. The expression of PGC1α, a transcriptional coactivator, is increased in the skeletal muscle during exercise. PGC1α activates branched-chain amino acid (BCAA) metabolism and is used for energy in the tricarboxylic acid (TCA) cycle. Leucine, a BCAA, and its metabolite, β-hydroxy-β-methylbutyrate (HMB), both activate mammalian target of rapamycin complex 1 (mTORC1) and increase protein synthesis, but the mechanisms of activation appear to be different. The metabolite of valine (another BCAA), β-aminoisobutyric acid (BAIBA), is increased by exercise, is secreted by the skeletal muscle, and acts on other tissues, such as white adipose tissue, to increase energy expenditure. In addition, several amino acid-related molecules reportedly activate skeletal muscle function. Oral 5-aminolevulinic acid (ALA) supplementation can protect against mild hyperglycemia and help prevent type 2 diabetes. β-alanine levels are decreased in the skeletal muscles of aged mice. β-alanine supplementation increased the physical performance and improved the executive function induced by endurance exercise in middle-aged individuals. Further studies focusing on the effects of amino acids and their metabolites on skeletal muscle function will provide data essential for the production of food supplements for older adults, athletes, and individuals with metabolic diseases.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

In Vitro Use of Cellular Synthetic Machinery for Biosensing Applications

The application of biosensors is expanding in diverse fields due to their high selectivity and sensitivity. Biosensors employ biological components for the recognition of target analytes. In addition, the amplifying nature of biosynthetic processes can potentially be harnessed to for biological transduction of detection signals. Recent advances in the development of highly productive and cost-effective cell-free synthesis systems make it possible to use these systems as the biological transducers to generate biosensing signals. This review surveys recent developments in cell-free biosensors, focusing on the newly devised mechanisms for the biological recognition of analytes to initiate the amplification processes of transcription and translation.

A multicentre clinical validation of AminoIndex Cancer Screening (AICS)

AminoIndex Cancer Screening (AICS) is a novel cancer screening test based on plasma free amino acid (PFAA) levels. This system categorises subjects as rank A, B, or C in order of increasing probability of each cancer incidence. The current study aimed to validate the potential of AICS for cancer detection. AICS values were determined from the PFAA levels in subjects examined at Chiba Cancer Center Cohort, Mitsui Memorial Hospital, and Saihaku Hospital, and the cancer incidence was investigated. The sensitivities of rank C for cancer diagnosis within 1 year after AICS examination were 83.3% (10/12) for gastric, 50.0% (2/4) for lung, 46.2% (6/13) for colorectal, 50.0% (8/16) for prostate, 43.8% (7/16) for breast, and 50.0% (1/2) for uterine/ovarian cancer. The total cancer detection rate via AICS was 0.33% (34/10,245). The sensitivities during the maximum follow-up period of 6.2 years were 51.7% (15/29) for gastric, 18.2% (2/11) for lung, 28.6% (8/28) for colorectal, 36.4% (8/22) for prostate, 29.0% (9/31) for breast, and 33.3% (2/6) for uterine/ovarian cancers. In conclusion, AICS is a more useful method for evaluating the probability of cancer incidence than for predicting onset, suggesting that annual AICS should be recommended to detect any malignancy.

Metabolic signatures of four major histological types of lung cancer cells

INTRODUCTION

Histologically lung cancer is classified into four major types: adenocarcinoma (Ad), squamous cell carcinoma (SqCC), large cell carcinoma (LCC), and small cell lung cancer (SCLC). Presently, our understanding of cellular metabolism among them is still not clear.

OBJECTIVES

The goal of this study was to assess the cellular metabolic profiles across these four types of lung cancer using an untargeted metabolomics approach.

METHODS

Six lung cancer cell lines, viz., Ad (A549 and HCC827), SqCC (NCl-H226 and NCl-H520), LCC (NCl-H460), and SCLC (NCl-H526), were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry, with normal human small airway epithelial cells (SAEC) as the control group. The principal component analysis (PCA) was performed to identify the metabolic signatures that had characteristic alterations in each histological type. Further, a metabolite set enrichment analysis was performed for pathway analysis.

RESULTS

Compared to the SAEC, 31, 27, 34, 34, 32, and 39 differential metabolites mainly in relation to nucleotides, amino acid, and fatty acid metabolism were identified in A549, HCC827, NCl-H226, NCl-H520, NCl-H460, and NCl-H526 cells, respectively. The metabolic signatures allowed the six cancerous cell lines to be clearly separated in a PCA score plot.

CONCLUSION

The metabolic signatures are unique to each histological type, and appeared to be related to their cell-of-origin and mutation status. The changes are useful for assessing the metabolic characteristics of lung cancer, and offer potential for the establishment of novel diagnostic tools for different origin and oncogenic mutation of lung cancer.

Post-operative AICS status in completely resected lung cancer patients with pre-operative AICS abnormalities: predictive significance of disease recurrence

The AminoIndex^TM Cancer Screening (AICS) system, a plasma-free amino acid (PFAA)-based multivariate discrimination index, is a blood screening test for lung cancer based on the comparison of PFAA concentrations between patients with lung cancer and healthy controls. Pre- and post-operative AICS values were compared among 72 patients who underwent curative resection for lung cancer. Post-operative changes in PFAA concentrations were also evaluated. AICS values were classified as rank A (0.0–4.9), B (5.0–7.9), or C (8.0–10.0). Rank B–C patients were evaluated for outcomes and post-operative changes in their AICS values. Twenty-three of the 44 pre-operative rank B–C patients experienced post-operative reductions in AICS rank. Only one patient experienced cancer recurrence. Post-operative changes in PFAA concentrations were associated with the risk of post-operative cancer recurrence (p = 0.001). Multivariate analysis revealed that the absence of a post-operative reduction in AICS rank independently predicted cancer recurrence (hazard ratio: 14.28; p = 0.012). The majority of patients had high pre-operative AICS values and exhibited a reduction in AICS rank after curative resection. However, the absence of a post-operative reduction in AICS rank was associated with cancer recurrence, suggesting that AICS rank may be a sensitive marker of post-operative recurrence.

Metabolomic analysis of serum from rats following long-term intake of Chinese sausage

Introduction

Owing to the contamination of chemical pollutants, especially nitrosamines and their precursors, in Chinese sausage, long-term intake of Chinese sausage may have potential health effects.

Objection

This study investigated the effects of long-term intake of Chinese sausage with different contaminations of N-nitrosodimethylamine (NDMA) on rat liver and the potential biomarkers in the serum.

Methods

Serum metabolomic analysis was performed by gas chromatography–mass spectrometry at weeks 7, 17, 25, and 33; simultaneously, liver histopathological examination was conducted and its relationship with the serum metabolomics was also investigated.

Results

In the study, long-term intake of Chinese sausage with different NDMA contents induced significant changes in serum metabolites and liver histopathology in rats. Metabonomic analysis showed that seven metabolites – β-alanine, 3-aminoisobutyric acid, aminooxyacetic acid, D-alanyl-D-alanine, pelargonic acid, palmitic acid (PA), and linoleic acid (LA) – in three sausage diet groups were significantly decreased at four time points, where three other metabolites were notably increased, which included putrescine, ethanolamine phosphate, and taurine. Among the various treatments, the NDMA (sausage-free) group demonstrated the most remarkable changes. Phenylalanine was decreased followed by an increase, and tyrosine persistently declined, both of which were elevated in the NDMA group. In addition, the histopathological result was consistent with that of the serum metabolomic analysis, and the changes in serum metabolites in each sausage diet group and the NDMA group were consistently associated with disorders of lipids, amino acid, and energy metabolism.

Conclusion

This work indicates that excessive NDMA content in sausage may cause liver damage.

Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile

Patients with gastric ulcer (GU) have a significantly higher risk of developing gastric cancer (GC), especially within 2 years after diagnosis. The main way to improve the prognosis of GC is to predict the tumorigenesis and metastasis in the early stage. The objective of this study was to demonstrate the ability of human plasma amino acid metabolic profile for discriminating GC and GU. In this study, we first used liquid chromatography-tandem mass spectrometry technique to characterize the plasma amino acid metabolism in GC and GU patients. Plasma samples were collected from 84 GC patients and 82 GU patients, and 22 amino acids were detected in each patient. Partial least squares-discriminant analysis model was performed to analyze the data of these amino acids. We observed seven differential amino acids between GC and GU. A regression analysis model was established using these seven amino acids. Finally, a panel of five differential amino acids, including glutamine, ornithine, histidine, arginine and tryptophan, was identified for discriminating GC and GU with good specificity and sensitivity. The receiver operating characteristic curve was used to evaluate diagnostic ability of the regression model and area under the curve was 0.922. In conclusion, this study demonstrated the potential values of plasma amino acid metabolic profile and metabolomic analysis technique in assisting diagnosis of GC. More studies are needed to highlight the theoretical strengths of metabolomics to understand the potential metabolic mechanisms in GC. © 2018 IUBMB Life, 70(6):553-562, 2018.

A Novel Concept of Amino Acid Supplementation to Improve the Growth of Young Malnourished Male Rats

BACKGROUNDS/AIMS

This study was aimed at understanding the relationship between plasma amino acids and protein malnutrition and at determining whether amino acid supplementation associated with malnutrition and growth improves linear growth in growing rats.

METHODS

Body length and plasma amino acids were measured in young male rats that were fed the following diet for 3 weeks, mimicking a low and imbalanced protein diets based on maize, a major staple consumed in developing countries: a 70% calorically restricted cornmeal-based diet (C), C + micronutrients (CM), CM + casein (CMC), CM + soy protein (CMS) or CMS + 0.3% lysine.

RESULTS

A correlation analysis of linear growth and plasma amino acids indicated that lysine, tryptophan, branched-chain amino acids, methionine, and phenylalanine significantly correlated with body length. Supplementation with these 5 amino acids (AA1) significantly improved the body length in rats compared to CMC treatment whereas, nitrogen-balanced amino acid supplemented controls (AA2) did not (CM +1.2 ± 0.2, CMC +2.7 ± 0.3, CMS +2.1 ± 0.3, AA1 +2.8 ± 0.2, and AA2 +2.5 ± 0.3 cm).

CONCLUSION

With securing proper amino acid balance, supplementing growth-related amino acids is more effective in improving linear growth in malnourished growing male rats. Analysis of the correlation between plasma amino acids and growth represents a powerful tool to determine candidate amino acids for supplementation to prevent malnutrition. This technology is adaptable to children in developing countries.

Perioperative dynamics and significance of plasma-free amino acid profiles in colorectal cancer

BACKGROUND

For early detection of cancer, we have previously developed the AminoIndex Cancer Screening (AICS) system, which quantifies 6 plasma-free amino acids (PFAAs) in blood samples. Herein, we examined the usefulness of the AICS in patients with colorectal cancer (CRC) by comparing the preoperative and postoperative PFAA profiles.

METHODS

Our study cohort consisted of 62 patients who had undergone curative resection for CRC at our cancer center, with no recurrence at the time of the study. Blood samples were collected from fasted patients within 1 week before the resection and at 0.5-6.5 years post-resection. Following plasmapheresis, the PFAA levels were measured via liquid chromatography/mass spectrometry, and the AICS values were computed (the higher the value, the greater the probability of cancer). Risk was calculated from the AICS value and ranked as A, B, or C, with rank C representing the highest risk. All patients in our study were rank B + C.

RESULTS

The postoperative AICS value was lower than the preoperative value in 57 of the 62 patients; the rank was also lower postoperatively (49 patients, p < 0.001). The decline in both was stage-independent, even occurring in patients with right-sided tumors or poorly differentiated adenocarcinomas. For comparative purposes, the levels of 2 tumor markers (carbohydrate antigen 19-9 and carcinoembryonic antigen) were also examined; these were within the reference ranges in 70-80% of patients preoperatively and in 80-90% postoperatively.

CONCLUSION

We suggest that tumor-bearing conditions alter the PFAA profiles, which may be used to predict prognosis and monitor for recurrence in CRC patients after tumor resection.

TRIAL REGISTRATION

This trial has been retrospectively registered at UMIN-CTR R000028005 , Oct 06, 2016.

Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14–3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.

Complementary Cell-Free Translational Assay for Quantification of Amino Acids

In this study, we present a simple and economical method that enables rapid quantification of amino acids based on their polymerization into a signal-generating protein. This method harnesses amino acid-deficient cell-free protein synthesis systems that generate fluorescence signals in response to exogenous amino acids. When premixed with assay samples containing the amino acids in question, incubation of the cell-free synthesis reaction mixture rapidly resulted in the production of sfGFP, the fluorescence intensity of which was linearly proportional to the concentration of the amino acids. The assay method achieved a limit of detection as low as ∼100 nM and was successfully applied to the quantification of disease-related amino acids in biological samples. Compared with standard methods in current use that require chemical derivatization of amino acids and chromatographic equipment, the complementation assay method developed in this work enables the direct translation of amino acid titer into measurable biofluorescence intensity in a much shorter period, providing a more affordable and flexible option for the quantification of amino acids.

Rapid differentiating colorectal cancer and colorectal polyp using dried blood spot mass spectrometry metabolomic approach

Colorectal cancer (CRC) is the third leading causes of cancer mortality, and the early-stage detection could significantly enhance survival rates. Cancer influences the important metabolic pathways and the changes in metabolite levels had been used in many studies as the potential biomarkers. This study is aimed at screening metabolite biomarkers with CRC diagnosis potentials. The direct infusion mass spectrometry (MS) metabolomic analysis based on dried blood spot was used to distinguish CRC from polyp. The target metabolites were composed of 23 amino acids and 26 acylcarnitines. The 21 metabolites in blood were selected via multivariate analysis. A regression model was established based on parameters C16, Arg, C4/C8, C5/C3, Val, Phe/Tyr, Ala, C4/C3. Tenfold cross validation (CV) method was used to test this model and showed sensitivity of 81.18% and specificity of 83.95%. The metabolomic analysis is a practicable method for CRC detection. The use of direct MS analysis in metabolite screening could be finished in several minutes and served as a higher-throughput method to distinguish CRC and polyps. © 2017 IUBMB Life, 69(5):347-354, 2017.

Spontaneous ultra-weak photon emission in correlation to inflammatory metabolism and oxidative stress in a mouse model of collagen-induced arthritis

The increasing prevalence of rheumatoid arthritis has driven the development of new approaches and technologies for investigating the pathophysiology of this devastating, chronic disease. From the perspective of systems biology, combining comprehensive personal data such as metabolomics profiling with ultra-weak photon emission (UPE) data may provide key information regarding the complex pathophysiology underlying rheumatoid arthritis. In this article, we integrated UPE with metabolomics-based technologies in order to investigate collagen-induced arthritis, a mouse model of rheumatoid arthritis, at the systems level, and we investigated the biological underpinnings of the complex dataset. Using correlation networks, we found that elevated inflammatory and ROS-mediated plasma metabolites are strongly correlated with a systematic reduction in amine metabolites, which is linked to muscle wasting in rheumatoid arthritis. We also found that increased UPE intensity is strongly linked to metabolic processes (with correlation co-efficiency |r| value >0.7), which may be associated with lipid oxidation that related to inflammatory and/or ROS-mediated processes. Together, these results indicate that UPE is correlated with metabolomics and may serve as a valuable tool for diagnosing chronic disease by integrating inflammatory signals at the systems level. Our correlation network analysis provides important and valuable information regarding the disease process from a system-wide perspective.

Proteomics and metabolomics in biomarker discovery for cardiovascular diseases: progress and potential

INTRODUCTION

The process of discovering novel biomarkers and potential therapeutic targets may be shortened using proteomic and metabolomic approaches.

AREAS COVERED

Several complementary strategies, each one presenting different advantages and limitations, may be used with these novel approaches. In vitro studies show how cells involved in cardiovascular disease react, although the phenotype of cultured cells differs to that occurring in vivo. Tissue analysis either in human specimens or animal models may show the proteins that are expressed in the pathological process, although the presence of structural proteins may be confounding. To identify circulating biomarkers, analyzing the secretome of cultured atherosclerotic tissue, analysis of blood cells and/or plasma may be more straightforward. However, in the latter approach, high-abundant proteins may mask small molecules that could be potential biomarkers. The study of sub-proteomes such as high-density lipoproteins may be useful to circumvent this limitation. Regarding metabolomics, most studies have been performed in small populations, and we need to perform studies in large populations in order to discover robust biomarkers. Expert commentary: It is necessary to involve the clinicians in these areas to improve the design of clinical studies, including larger populations, in order to obtain consistent novel biomarkers.

A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection

OBJECTIVE

Breast cancer (BC) is still a lethal threat to women worldwide. An accurate screening and diagnosis strategy performed in an easy-to-operate manner is highly warranted in clinical perspective. Besides the routinely focused protein markers, blood is full of small molecular metabolites with diverse structures and properties. This study aimed to screen metabolite markers with BC diagnosis potentials.

METHODS

A dried blood spot-based direct infusion mass spectrometry (MS) metabolomic analysis was conducted for BC and non-BC differentiation. The targeted analytes included 23 amino acids and 26 acylcarnitines.

RESULTS

Multivariate analysis screened out 21 BC-related metabolites in the blood. Regression analysis generated a diagnosis model consisting of parameters Pip, Asn, Pro, C14:1/C16, Phe/Tyr, and Gly/Ala. Tested with another set of BC and non-BC samples, this model showed a sensitivity of 92.2% and a specificity of 84.4%. Compared to the routinely used protein markers, this model exhibited distinct advantage with its higher sensitivity.

CONCLUSION

Blood metabolites screening is a more plausible approach for BC detection. Furthermore, this direct MS analysis could be finished within few minutes, which means that its throughput is higher than the currently used imaging techniques.

Derivatization methods for LC–MS analysis of endogenous compounds

Sensitive and reliable analysis of endogenous compounds is critically important for many physiological and pathological studies. Methods based on LC–MS have progressed to become the method of choice for analyzing endogenous compounds. However, the analysis can be challenging due to various factors, including inherent low concentrations in biological samples, low ionization efficiency, undesirable chromatographic behavior and interferences of complex biological. The integration of chemical derivatization with LC–MS could enhance its capabilities in sensitivity and selectivity, and extend its application to a wider range of analytes. In this article, we will review the derivatization strategies in the LC–MS analysis of various endogenous compounds, and provide applications highlighting the impact of these important techniques in the evaluation of pathological events.

Plasma Free Amino Acid Profiles Predict Four-Year Risk of Developing Diabetes, Metabolic Syndrome, Dyslipidemia, and Hypertension in Japanese Population

Plasma free amino acid (PFAA) profile is highlighted in its association with visceral obesity and hyperinsulinemia, and future diabetes. Indeed PFAA profiling potentially can evaluate individuals' future risks of developing lifestyle-related diseases, in addition to diabetes. However, few studies have been performed especially in Asian populations, about the optimal combination of PFAAs for evaluating health risks. We quantified PFAA levels in 3,701 Japanese subjects, and determined visceral fat area (VFA) and two-hour post-challenge insulin (Ins120 min) values in 865 and 1,160 subjects, respectively. Then, models between PFAA levels and the VFA or Ins120 min values were constructed by multiple linear regression analysis with variable selection. Finally, a cohort study of 2,984 subjects to examine capabilities of the obtained models for predicting four-year risk of developing new-onset lifestyle-related diseases was conducted. The correlation coefficients of the obtained PFAA models against VFA or Ins120 min were higher than single PFAA level. Our models work well for future risk prediction. Even after adjusting for commonly accepted multiple risk factors, these models can predict future development of diabetes, metabolic syndrome, and dyslipidemia. PFAA profiles confer independent and differing contributions to increasing the lifestyle-related disease risks in addition to the currently known factors in a general Japanese population.

Unexpected similarities between the Schizosaccharomyces and human blood metabolomes, and novel human metabolites

Metabolomics, a modern branch of chemical biology, provides qualitative and quantitative information about the metabolic states of organisms or cells at the molecular level. Here we report non-targeted, metabolomic analyses of human blood, using liquid chromatography-mass spectrometry (LC-MS). We compared the blood metabolome to the previously reported metabolome of the fission yeast, Schizosaccharomyces pombe. The two metabolomic datasets were highly similar: 101 of 133 compounds identified in human blood (75%) were also present in S. pombe, and 45 of 57 compounds enriched in red blood cells (RBCs) (78%) were also present in yeast. The most abundant metabolites were ATP, glutathione, and glutamine. Apart from these three, the next most abundant metabolites were also involved in energy metabolism, anti-oxidation, and amino acid metabolism. We identified fourteen new blood compounds, eight of which were enriched in RBCs: citramalate, GDP-glucose, trimethyl-histidine, trimethyl-phenylalanine, trimethyl-tryptophan, trimethyl-tyrosine, UDP-acetyl-glucosamine, UDP-glucuronate, dimethyl-lysine, glutamate methyl ester, N-acetyl-(iso)leucine, N-acetyl-glutamate, N2-acetyl-lysine, and N6-acetyl-lysine. Ten of the newly identified blood metabolites were also detected in S. pombe, and ten of the 14 newly identified blood metabolites were methylated or acetylated amino acids. Trimethylated or acetylated free amino acids were also abundant in white blood cells. It may be possible to investigate their physiological roles using yeast genetics.

A novel amino acid analysis method using derivatization of multiple functional groups followed by liquid chromatography/tandem mass spectrometry

We have developed a novel amino acid analysis method using derivatization of multiple functional groups (amino, carboxyl, and phenolic hydroxyl groups).

Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes

Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64–0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62–0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57–5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.

Disruption of behavioral circadian rhythms induced by psychophysiological stress affects plasma free amino acid profiles without affecting peripheral clock gene expression in mice

Disordered circadian rhythms are associated with various psychiatric conditions and metabolic diseases. We recently established a mouse model of a psychophysiological stress-induced chronic sleep disorder (CSD) characterized by reduced amplitude of circadian wheel-running activity and sleep-wake cycles, sleep fragmentation and hyperphagia. Here, we evaluate day-night fluctuations in plasma concentrations of free amino acids (FAA), appetite hormones and prolactin as well as the hepatic expression of circadian clock-related genes in mice with CSD (CSD mice). Nocturnal increases in wheel-running activity and circadian rhythms of plasma prolactin concentrations were significantly disrupted in CSD mice. Hyperphagia with a decreased leptin/ghrelin ratio was found in CSD mice. Day-night fluctuations in plasma FAA contents were severely disrupted without affecting total FAA levels in CSD mice. Nocturnal increases in branched-chain amino acids such as Ile, Leu, and Val were further augmented in CSD mice, while daytime increases in Gly, Ala, Ser, Thr, Lys, Arg, His, Tyr, Met, Cys, Glu, and Asn were significantly attenuated. Importantly, the circadian expression of hepatic clock genes was completely unaffected in CSD mice. These findings suggest that circadian clock gene expression does not always reflect disordered behavior and sleep rhythms and that plasma FFA profiles could serve as a potential biomarker of circadian rhythm disorders.

Exploratory investigation of plasma metabolomics in human lung adenocarcinoma

Globally lung cancer is common among males and recently also noted with increasing incidences in females, especially adenocarcinoma. Further, most lung cancers are not easily detected until the late stage. Metabolic profiling of plasma low molecular weight metabolites may help unveil the complex pathophysiological changes during early lung adenocarcinoma development. Here we used a combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods to investigate the metabolic signatures in the plasma of 31 stage I human lung adenocarcinoma patients and 28 healthy controls. The metabolic profiles were assayed using orthogonal projections to latent structures discriminant analysis (OPLS-DA), and were further analyzed to identify the associated marker metabolites. The OPLS-DA models derived from both GC-MS and LC-MS showed significant discriminations in metabolic profiles between cases and healthy controls. It was found that around 37 metabolites contributed to the differences. The alterations of these metabolites implied disturbances in amino acids, lipids, fatty acids and glutaminolysis metabolism in human lung adenocarcinoma, even after removal of influencing factors such as age, gender and smoking habits. Of particular interest, the sex hormone metabolic pathway involving the sulfate conjugate of testosterone, androsterone and pregnenolone was found to be disturbed considerably. All these metabolic perturbations occur at an early stage of lung adenocarcinoma and thus could act as biomarkers for its early diagnosis. These exploratory findings suggest that integration of two sensitive and complementary metabolomic approaches enables a comprehensive metabolite profiling for human lung adenocarcinoma, although a more extensive study is needed to confirm the findings.

A new approach for quantitative analysis of L-phenylalanine using a novel semi-sandwich immunometric assay

Here, we describe a novel method for L-phenylalanine analysis using a sandwich-type immunometric assay approach for use as a new method for amino acid analysis. To overcome difficulties of the preparation of high-affinity and selectivity monoclonal antibodies against L-phenylalanine and the inability to use sandwich-type immunometric assays due to their small molecular weight, three procedures were examined. First, amino groups of L-phenylalanine were modified by "N-Fmoc-L-cysteine" (FC) residues and the derivative (FC-Phe) was used as a hapten. Immunization of mice with bovine serum albumin/FC-Phe conjugate successfully yielded specific monoclonal anti-FC-Phe antibodies. Second, a new derivatization reagent, "biotin linker conjugate of FC-Phe N-succinimidyl ester" (FC(Biotin)-NHS), was synthesized to convert L-phenylalanine to FC-(Biotin)-Phe as a hapten structure. The biotin moiety linked to the thiol group of cysteine formed a second binding site for streptavidin/horseradish peroxidase (HRP) conjugates for optical detection. Third, a new semi-sandwich-type immunometric assay was established using pre-derivatized L-phenylalanine, the monoclonal anti-FC-Phe antibody, and streptavidin/HRP conjugate (without second antibody). Using the new "semi-sandwich" immunometric assay system, a detection limit of 35 nM (60 amol per analysis) and a detection range of 0.1-20 μM were attained using a standard L-phenylalanine solution. Rat plasma samples were analyzed to test reliability. Intra-day assay precision was within 6% of the coefficient of variation; inter-day variation was 0.1%. The recovery rates were from 92.4 to 123.7%. This is the first report of the quantitative determination of L-phenylalanine using a reliable semi-sandwich immunometric assay approach and will be applicable to the quantitative determination of other amino acids.

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance ((1)H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n=25) and healthy controls (n=25) was performed by using both (1)H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.

The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

Background

We have recently reported on the changes in plasma free amino acid (PFAA) profiles in lung cancer patients and the efficacy of a PFAA-based, multivariate discrimination index for the early detection of lung cancer. In this study, we aimed to verify the usefulness and robustness of PFAA profiling for detecting lung cancer using new test samples.

Methods

Plasma samples were collected from 171 lung cancer patients and 3849 controls without apparent cancer. PFAA levels were measured by high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS).

Results

High reproducibility was observed for both the change in the PFAA profiles in the lung cancer patients and the discriminating performance for lung cancer patients compared to previously reported results. Furthermore, multivariate discriminating functions obtained in previous studies clearly distinguished the lung cancer patients from the controls based on the area under the receiver-operator characteristics curve (AUC of ROC = 0.731 ~ 0.806), strongly suggesting the robustness of the methodology for clinical use. Moreover, the results suggested that the combinatorial use of this classifier and tumor markers improves the clinical performance of tumor markers.

Conclusions

These findings suggest that PFAA profiling, which involves a relatively simple plasma assay and imposes a low physical burden on subjects, has great potential for improving early detection of lung cancer.

Multivariable analysis of host amino acids in plasma and liver during infection of malaria parasite Plasmodium yoelii

BACKGROUND

Malaria is the most significant human parasitic disease, and yet understanding of the energy metabolism of the principle pathogen, Plasmodium falciparum, remains to be fully elucidated. Amino acids were shown to be essential nutritional requirements since early times and much of the current knowledge of Plasmodium energy metabolism is based on early biochemical work, performed using basic analytical techniques, carried out almost exclusively on human plasma with considerable inter-individual variability.

METHODS

In order to further characterize the fate of amino acid metabolism in malaria parasite, multivariate analysis using statistical modelling of amino acid concentrations (aminogram) of plasma and liver were determined in host infected with rodent malaria parasite, Plasmodium yoelii.

RESULTS AND CONCLUSION

Comprehensive and statistical aminogram analysis revealed that P. yoelii infection caused drastic change of plasma and liver aminogram, and altered intra- and inter-correlation of amino acid concentration in plasma and liver. These findings of the interactions between amino acids and Plasmodium infection may provide insight to reveal the interaction between nutrients and parasites.

A novel serum metabolomics-based diagnostic approach for colorectal cancer

BACKGROUND

To improve the quality of life of colorectal cancer patients, it is important to establish new screening methods for early diagnosis of colorectal cancer.

METHODOLOGY/PRINCIPAL FINDINGS

We performed serum metabolome analysis using gas-chromatography/mass-spectrometry (GC/MS). First, the accuracy of our GC/MS-based serum metabolomic analytical method was evaluated by calculating the RSD% values of serum levels of various metabolites. Second, the intra-day (morning, daytime, and night) and inter-day (among 3 days) variances of serum metabolite levels were examined. Then, serum metabolite levels were compared between colorectal cancer patients (N = 60; N = 12 for each stage from 0 to 4) and age- and sex-matched healthy volunteers (N = 60) as a training set. The metabolites whose levels displayed significant changes were subjected to multiple logistic regression analysis using the stepwise variable selection method, and a colorectal cancer prediction model was established. The prediction model was composed of 2-hydroxybutyrate, aspartic acid, kynurenine, and cystamine, and its AUC, sensitivity, specificity, and accuracy were 0.9097, 85.0%, 85.0%, and 85.0%, respectively, according to the training set data. In contrast, the sensitivity, specificity, and accuracy of CEA were 35.0%, 96.7%, and 65.8%, respectively, and those of CA19-9 were 16.7%, 100%, and 58.3%, respectively. The validity of the prediction model was confirmed using colorectal cancer patients (N = 59) and healthy volunteers (N = 63) as a validation set. At the validation set, the sensitivity, specificity, and accuracy of the prediction model were 83.1%, 81.0%, and 82.0%, respectively, and these values were almost the same as those obtained with the training set. In addition, the model displayed high sensitivity for detecting stage 0-2 colorectal cancer (82.8%).

CONCLUSIONS/SIGNIFICANCE

Our prediction model established via GC/MS-based serum metabolomic analysis is valuable for early detection of colorectal cancer and has the potential to become a novel screening test for colorectal cancer.

Plasma amino acid profile is associated with visceral fat accumulation in obese Japanese subjects

What is already known about this subject •  Asians with metabolic complications associated with obesity, a low body mass index and a low waist circumference have a greater proportion of visceral adipose tissue for a given amount of total body fat compared with Europeans. •  Apparent obese humans and obese animal models show an elevation of branched-chain amino acid levels in plasma. •  A multivariate logistic regression model of plasma free amino acids has been used to screen for several types of cancers in clinical settings. What this study adds •  A specific formula incorporating six amino acid values (Ala, Gly, Glu, Trp, Tyr and branched-chain amino acid) was developed for discrimination of subjects with high visceral fat area by multivariate logistic regression analyses. •  The generated amino acid formula was strongly correlated with visceral fat area in both apparent and non-apparent obese subjects. •  Measuring plasma free amino acids can be used to distinguish the non-apparent visceral obesity in clinical settings in Asian populations.

SUMMARY

Metabolic complications associated with obesity are becoming more common among Japanese subjects. However, visceral fat accumulation is not always apparent by measuring body mass index (BMI) or waist circumference in Asian populations because of the physiological characteristics particular to those ethnicities. Excess visceral fat accumulation raises the odds ratio for developing cardiovascular disease. Thus, high-throughput determination of the amount of abdominal adipose tissue is necessary. We hypothesized that accumulating visceral fat alters the peripheral amino acid profile and that a multivariate logistic regression model of plasma free amino acids can distinguish visceral obesity. A total of 1449 Japanese subjects (985 males and 464 females) who had undergone a comprehensive health screening were enrolled in this study. The visceral fat area was determined using computed tomography imaging, and a plasma free amino acid index to identify high visceral fat areas (≥100 cm(2) ) was developed. The sensitivity and specificity values of the generated amino acid index were 80% and 65%, respectively. In particular, the sensitivity of the generated index to identify subjects with non-apparent visceral obesity (BMI < 25 kg m(-2) ; visceral fat area ≥ 100 cm(2) ) was much greater than that of the waist circumference (73% vs. 46%, respectively). This index's high sensitivity and specificity may be the result of specific alterations in the patients' amino acid profiles, which were specifically correlated with the visceral fat areas and not with subcutaneous fat areas. This profile can be used as a predictor of elevated visceral obesity and a risk assessment tool for metabolic complications in Asian populations.

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Possibility of multivariate function composed of plasma amino acid profiles as a novel screening index for non-small cell lung cancer: a case control study

Background

The amino-acid balance in cancer patients often differs from that in healthy individuals, because of metabolic changes. This study investigated the use of plasma amino-acid profiles as a novel marker for screening non-small-cell lung cancer (NSCLC) patients.

Methods

The amino-acid concentrations in venous blood samples from pre-treatment NSCLC patients (n = 141), and age-matched, gender-matched, and smoking status-matched controls (n = 423), were measured using liquid chromatography and mass spectrometry. The resultant study data set was subjected to multiple logistic regression analysis to identify amino acids related with NSCLC and construct the criteria for discriminating NSCLC patients from controls. A test data set derived from 162 patients and 3,917 controls was used to validate the stability of the constructed criteria.

Results

The plasma amino-acid profiles significantly differed between the NSCLC patients and the controls. The obtained model (including alanine, valine, isoleucine, histidine, tryptophan and ornithine concentrations) performed well, with an area under the curve of the receiver-operator characteristic curve (ROC_AUC) of >0.8, and allowed NSCLC patients and controls to be discriminated regardless of disease stage or histological type.

Conclusions

This study shows that plasma amino acid profiling will be a potential screening tool for NSCLC.

Characteristic metabolism of free amino acids in cetacean plasma: cluster analysis and comparison with mice

From an evolutionary perspective, the ancestors of cetaceans first lived in terrestrial environments prior to adapting to aquatic environments. Whereas anatomical and morphological adaptations to aquatic environments have been well studied, few studies have focused on physiological changes. We focused on plasma amino acid concentrations (aminograms) since they show distinct patterns under various physiological conditions. Plasma and urine aminograms were obtained from bottlenose dolphins, pacific white-sided dolphins, Risso's dolphins, false-killer whales and C57BL/6J and ICR mice. Hierarchical cluster analyses were employed to uncover a multitude of amino acid relationships among different species, which can help us understand the complex interrelations comprising metabolic adaptations. The cetacean aminograms formed a cluster that was markedly distinguishable from the mouse cluster, indicating that cetaceans and terrestrial mammals have quite different metabolic machinery for amino acids. Levels of carnosine and 3-methylhistidine, both of which are antioxidants, were substantially higher in cetaceans. Urea was markedly elevated in cetaceans, whereas the level of urea cycle-related amino acids was lower. Because diving mammals must cope with high rates of reactive oxygen species generation due to alterations in apnea/reoxygenation and ischemia-reperfusion processes, high concentrations of antioxidative amino acids are advantageous. Moreover, shifting the set point of urea cycle may be an adaption used for body water conservation in the hyperosmotic sea water environment, because urea functions as a major blood osmolyte. Furthermore, since dolphins are kept in many aquariums for observation, the evaluation of these aminograms may provide useful diagnostic indices for the assessment of cetacean health in artificial environments in the future.