Amino acid profile index for early detection of endometrial cancer: verification as a novel diagnostic marker.

BACKGROUND: Plasma amino acid profiles (PAAPs) vary in individual cancer patients, and it has been suggested that they may be useful for early detection of several types of cancer. We evaluated the diagnostic performance of a profile index for endometrial cancer composed of multiple plasma amino acids as a novel biomarker and compared its diagnostic performance with that of CA125. METHODS: Plasma amino acid levels of 80 patients with endometrial cancer, 122 with benign gynecological diseases, and 240 age- and body mass index-matched control subjects were measured using liquid chromatography and mass spectrometry. After univariate analysis, we applied a multiplex model based on the PAAP multivariate analysis to distinguish patients with endometrial cancer from control subjects. We compared the diagnostic performance of the multiple PAAP index (API) with that of CA125. RESULTS: The levels of several plasma amino acids were significantly different in patients with endometrial cancer. The area under the receiver operating characteristic curves (AUC) used to distinguish endometrial cancer patients from control subjects was 0.94. The AUC for API was significantly larger than that for CA125 (P = 0.0068). For the same specificity of 98.3 %, API showed a significantly higher sensitivity (60.0 %, 95 % CI, 43.3-75.1) than that of CA125 (22.5 %, 95 % CI, 10.1-38.5). In stage I cases, API showed significantly higher positivity than that of CA125 (P = 0.0002). CONCLUSIONS: The sensitivity and disease specificity of API for early-stage detection of endometrial cancer was superior to CA125. This novel plasma biomarker has the potential to become a diagnostic and screening marker for endometrial cancer.

Malnutrition impairs interferon signaling through mTOR and FoxO pathways in patients with chronic hepatitis C.

BACKGROUND & AIMS: Patients with advanced chronic hepatitis C (CH-C) often are malnourished, but the effects of malnutrition on interferon (IFN) signaling and response to treatment have not been determined. We assessed the importance of the nutritional state of the liver on IFN signaling and treatment response. METHODS: We studied data from 168 patients with CH-C who were treated with the combination of pegylated-IFN and ribavirin. Plasma concentrations of amino acids were measured by mass spectrometry. Liver gene expression profiles were obtained from 91 patients. Huh-7 cells were used to evaluate the IFN signaling pathway, mammalian target of rapamycin complex 1 (mTORC1), and forkhead box O (FoxO). Antiviral signaling induced by branched-chain amino acids (BCAAs) was determined using the in vitro hepatitis C virus replication system. RESULTS: Multivariate logistic regression analysis showed that Fischer's ratio was associated significantly with nonresponders, independent of interleukin-28B polymorphisms or the histologic stage of the liver. Fischer's ratio was correlated inversely with the expression of BCAA transaminase 1, and was affected by hepatic mTORC1 signaling. IFN stimulation was impaired substantially in Huh-7 cells grown in medium that was low in amino acid concentration, through repressed mTORC1 signaling, and increased Socs3 expression, which was regulated by Foxo3a. BCAA could restore impaired IFN signaling and inhibit hepatitis C virus replication under conditions of malnutrition. CONCLUSIONS: Malnutrition impaired IFN signaling by inhibiting mTORC1 and activating Socs3 signaling through Foxo3a. Increasing BCAAs to up-regulate IFN signaling might be used as a new therapeutic approach for patients with advanced CH-C.

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model.

We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity. Based on our assumption that whole structural segments of ligands, GPCRs and G-proteins are essential to determine GPCR and G-protein coupling, various quantitative features were selected for ligands, GPCRs and G-protein complex structures, and those parameters that are the most effective in selecting G-protein type were used as feature vectors in the SVM. The main part of GRIFFIN includes a hierarchical SVM classifier using the feature vectors, which is useful for Class A GPCRs, the major family. For the opsins and olfactory subfamilies of Class A and other minor families (Classes B, C, frizzled and smoothened), the binding G-protein is predicted with high accuracy using the HMM. Applying this system to known GPCR sequences, each binding G-protein is predicted with high sensitivity and specificity (>85% on average). GRIFFIN (http://griffin.cbrc.jp/) is freely available and allows users to easily execute this reliable prediction of G-proteins.

Statistical analysis and prediction of functional residues effective for GPCR-G-protein coupling selectivity.

One of the important issues in G-protein-coupled receptor (GPCR) functional analysis is the mechanism of GPCR-G-protein coupling selectivity. G-proteins are classified into Gi/o, Gq/11 and Gs families. Although several experimental and computational analyses have been attempted, the mechanism remains unknown to this day. In this study, we have analyzed the multiple sequence alignments of GPCRs of known coupling selectivities by mapping onto the tertiary structure of rhodopsin. We identified several functional residue sites in GPCRs related to coupling selectivity, which are located mainly at the intracellular loops, and found that the occurrence of positively/negatively charged amino acids of the characteristic residues varies depending on the G-protein coupling selectivity. Especially, the occurrence of positively charged amino acids in receptors coupling to Gs family is less than that in receptors coupling to Gi/o and Gq/11 families. It is interesting that some characteristic residues are located near the extracellular terminus of transmembrane helices, which is far from the GPCR/G-protein binding interface. In most of the receptors coupling to Gs family, the occurrence of proline on the position corresponding to the 170th residue on rhodopsin is rare. These findings are vital to improving our understanding of the mechanism of G-protein coupling selectivity.

Reference intervals for plasma-free amino acid in a Japanese population.

BACKGROUND: Plasma amino acid concentrations vary with various diseases. Although reference intervals are useful in daily clinical practice, no reference intervals have been reported for plasma amino acids in a large Japanese population. METHODS: Reference individuals were selected from 7685 subjects examined with the Japanese Ningen Dock in 2008. A total of 1890 individuals were selected based on exclusion criteria, and the reference samples were selected after the outlier samples for each amino acid concentration were excluded. The lower limit of the reference intervals for the plasma amino acid concentrations was set at the 2.5th percentile and the upper limit at the 97.5th percentile. RESULTS: By use of the nested analysis of variance, we analysed a large dataset of plasma samples and the effects of background factors (sex, age and body mass index [BMI]) on the plasma amino acid concentrations. Most amino acid concentrations were related to sex, especially those of branched-chained amino acid. The citrulline, glutamine, ornithine and lysine concentrations were related to age. The glutamate concentration was related to body mass index. CONCLUSIONS: The concentrations of most amino acids are more strongly related to sex than to age or body mass index. Our results indicate that the reference intervals for plasma amino acid concentrations should be stratified by sex when the background factors of age and body mass index are considered.

Validation of an analytical method for human plasma free amino acids by high-performance liquid chromatography ionization mass spectrometry using automated precolumn derivatization.

The analysis of human plasma free amino acids is important for diagnosing the health of individuals, because their concentrations are known to vary with various diseases. The development of valid, reliable, and high-throughput analytical methods for amino acids analysis is an essential requirement in clinical applications. In the present study, we have developed an automated precolumn derivatization amino acid analytical method based on high-performance liquid chromatography/electrospray ionization mass spectrometry (so-called UF-Amino Station). This method enabled the separation of at least 38 types of physiological amino acids within 8min, and the interval time between injections was 12min. We also validated this method for 21 major types of free amino acids in human plasma samples. The results of the specificity, linearity, accuracy, repeatability, intermediate precision, reproducibility, limits of detections, lower limits of quantification, carry over, and sample solution stability were sufficient to allow for the measurement of amino acids in human plasma samples. Our developed method should be suitable for use in clinical fields.

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.

Novel, objective, multivariate biomarkers composed of plasma amino acid profiles for the diagnosis and assessment of inflammatory bowel disease.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic intestinal disorder that is associated with a limited number of clinical biomarkers. In order to facilitate the diagnosis of IBD and assess its disease activity, we investigated the potential of novel multivariate indexes using statistical modeling of plasma amino acid concentrations (aminogram). METHODOLOGY AND PRINCIPAL FINDINGS: We measured fasting plasma aminograms in 387 IBD patients (Crohn's disease (CD), n = 165; ulcerative colitis (UC), n = 222) and 210 healthy controls. Based on Fisher linear classifiers, multivariate indexes were developed from the aminogram in discovery samples (CD, n = 102; UC, n = 102; age and sex-matched healthy controls, n = 102) and internally validated. The indexes were used to discriminate between CD or UC patients and healthy controls, as well as between patients with active disease and those in remission. We assessed index performances using the area under the curve of the receiver operating characteristic (ROC AUC). We observed significant alterations to the plasma aminogram, including histidine and tryptophan. The multivariate indexes established from plasma aminograms were able to distinguish CD or UC patients from healthy controls with ROC AUCs of 0.940 (95% confidence interval (CI): 0.898-0.983) and 0.894 (95%CI: 0.853-0.935), respectively in validation samples (CD, n = 63; UC, n = 120; healthy controls, n = 108). In addition, other indexes appeared to be a measure of disease activity. These indexes distinguished active CD or UC patients from each remission patients with ROC AUCs of 0.894 (95%CI: 0.853-0.935) and 0.849 (95%CI: 0.770-0.928), and correlated with clinical disease activity indexes for CD (r(s) = 0.592, 95%CI: 0.385-0.742, p<0.001) or UC (r(s) = 0.598, 95%CI: 0.452-0.713, p<0.001), respectively. CONCLUSIONS AND SIGNIFICANCE: In this study, we demonstrated that established multivariate indexes composed of plasma amino acid profiles can serve as novel, non-invasive, objective biomarkers for the diagnosis and monitoring of IBD, providing us with new insights into the pathophysiology of the disease.