Awake functional MRI detects neural circuit dysfunction in a mouse model of autism.

MRI has potential as a translational approach from rodents to humans. However, given that mouse functional MRI (fMRI) uses anesthetics for suppression of motion, it has been difficult to directly compare the result of fMRI in "unconsciousness" disease model mice with that in "consciousness" patients. We develop awake fMRI to investigate brain function in 15q dup mice, a copy number variation model of autism. Compared to wild-type mice, we find that 15q dup is associated with whole-brain functional hypoconnectivity and diminished fMRI responses to odors of stranger mice. Ex vivo diffusion MRI reveals widespread anomalies in white matter ultrastructure in 15q dup mice, suggesting a putative anatomical substrate for these functional hypoconnectivity. We show that d-cycloserine (DCS) treatment partially normalizes these anormalies in the frontal cortex of 15q dup mice and rescues some social behaviors. Our results demonstrate the utility of awake rodent fMRI and provide a rationale for further investigation of DCS therapy.

Tissue amino acid profiles are characteristic of tumor type, malignant phenotype, and tumor progression in pancreatic tumors.

Tissue amino acid profiles depend on the cell types and extracellular components that constitute the tissue, and their functions and activities. We aimed to characterize the tissue amino acid profiles in several types of pancreatic tumors and lesions. We examined tissue amino acid profiles in 311 patients with pancreatic tumors or lesions. We used newly developed LC-MS/MS methods to obtain the profiles, which were compared with clinicopathological data. Each tumor or lesion presented a characteristic tissue amino acid profile. Certain amino acids were markedly altered during the multistep pancreatic carcinogenesis and pancreatic ductal adenocarcinoma (PDAC) progression. A tissue amino acid index (TAAI) was developed based on the amino acids that were notably changed during both carcinogenesis and cancer progression. Univariate and multivariate survival analyses revealed that PDAC patients with a high TAAI exhibited a significantly shorter survival rate, and these findings were validated using a second cohort. We suggest that tissue amino acid profiles are characteristic for normal tissue type, tumor histological type, and pathological lesion, and are representative of the cancer grade or progression stage in multistep carcinogenesis and of malignant characteristics. The TAAI could serve as an independent prognosticator for patients with PDAC.

Decreased Plasma Histidine Level Predicts Risk of Relapse in Patients with Ulcerative Colitis in Remission.

Ulcerative colitis (UC) is characterized by chronic intestinal inflammation. Patients with UC have repeated remission and relapse. Clinical biomarkers that can predict relapse in UC patients in remission have not been identified. To facilitate the prediction of relapse of UC, we investigated the potential of novel multivariate indexes using statistical modeling of plasma free amino acid (PFAA) concentrations. We measured fasting PFAA concentrations in 369 UC patients in clinical remission, and 355 were observed prospectively for up to 1 year. Relapse rate within 1 year was 23% (82 of 355 patients). The age- and gender-adjusted hazard ratio for the lowest quartile compared with the highest quartile of plasma histidine concentration was 2.55 (95% confidence interval: 1.41-4.62; p = 0.0020 (log-rank), p for trend = 0.0005). We demonstrated that plasma amino acid profiles in UC patients in clinical remission can predict the risk of relapse within 1 year. Decreased histidine level in PFAAs was associated with increased risk of relapse. Metabolomics could be promising for the establishment of a non-invasive predictive marker in inflammatory bowel disease.

A Novel Multivariate Index for Pancreatic Cancer Detection Based On the Plasma Free Amino Acid Profile.

BACKGROUND: The incidence of pancreatic cancer (PC) continues to increase in the world, while most patients are diagnosed with advanced stages and survive <12 months. This poor prognosis is attributable to difficulty of early detection. Here we developed and evaluated a multivariate index composed of plasma free amino acids (PFAAs) for early detection of PC. METHODS: We conducted a cross-sectional study in multi-institutions in Japan. Fasting plasma samples from PC patients (n = 360), chronic pancreatitis (CP) patients (n = 28), and healthy control (HC) subjects (n = 8372) without apparent cancers who were undergoing comprehensive medical examinations were collected. Concentrations of 19 PFAAs were measured by liquid chromatography-mass spectrometry. We generated an index consisting of the following six PFAAs: serine, asparagine, isoleucine, alanine, histidine, and tryptophan as variables for discrimination in a training set (120 PC and matching 600 HC) and evaluation in a validation set (240 PC, 28 CP, and 7772 HC). RESULTS: Several amino acid concentrations in plasma were significantly altered in PC. Plasma tryptophan and histidine concentrations in PC were particularly low, while serine was particularly higher than that of HC. The area under curve (AUC) based on receiver operating characteristic (ROC) curve analysis of the resulting index to discriminate PC from HC were 0.89 [95% confidence interval (CI), 0.86-0.93] in the training set. In the validation set, AUCs based on ROC curve analysis of the PFAA index were 0.86 (95% CI, 0.84-0.89) for all PC patients versus HC subjects, 0.81 (95% CI, 0.75-0.86) for PC patients from stage IIA to IIB versus HC subjects, and 0.87 (95% CI, 0.80-0.93) for all PC patients versus CP patients. CONCLUSIONS: These findings suggest that the PFAA profile of PC was significantly different from that of HC. The PFAA index is a promising biomarker for screening and diagnosis of PC.

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.

Amino Acid profiles in human tear fluids analyzed by high-performance liquid chromatography and electrospray ionization tandem mass spectrometry.

PURPOSE: To identify the 23 amino acid profiles in human tear fluids, and to evaluate whether the ocular disease conditions reflect the amino acid profiles. DESIGN: Laboratory investigation. METHODS: We evaluated the concentrations and relative composition of 23 amino acids in tear fluids obtained from 31 healthy volunteers using reversed-phase high-performance liquid chromatography and electrospray ionization tandem mass spectrometry, and compared them with those in plasma and aqueous humor. We also evaluated the tear-fluid amino acid profiles from 33 affected subjects. RESULTS: The amino acid profiles of the basal tear and reflex tear were found to be similar, and 4 distinct groups of healthy volunteers (male, female, young, and elderly) showed similar profiles. Absolute concentrations of taurine (Tau) and L-glutamine were significantly dominant in these tear fluids. The relative compositions of Tau, L-glutamic acid, L-arginine (Arg), and citrulline in the tear fluid were significantly higher than those in the plasma and aqueous humor. Analysis of the hierarchical clustering of the amino acid profiles clearly distinguished severe ocular surface diseases from non-ocular surface diseases. The relative compositions of Tau, L-methionine, and Arg decreased in severe ocular surface disease subjects compared with non-ocular surface disease subjects. CONCLUSIONS: Tear-fluid amino acid profiles differ from those in plasma and aqueous humor. Steady-state tear-fluid amino acid profiles might reflect ocular-surface homeostasis and the observed changes of amino acids might have a close relation with the disease conditions on the ocular surface.

Replicated studentized-deviate detection applied to the identification of differentially expressed genes in TNF-alpha-stimulated cells.

Among the applications of DNA microarray expression data, identification of differentially expressed genes is widely used to search for genes involved in specific biological processes. Previous studies have shown that replication is necessary to reliably identify differentially expressed genes. In practice, however, further guidelines are required to provide references for experiment design, that is, to fulfill the needs for determining the replication number on a desired confidence level or the detection sensitivity of the microarrays. Here, we performed simulations to build a simple statistical table of general guidelines adapted specifically for identifying differentially expressed genes using replications of a microarray experiment, and we present an accompanying nonparametric method, replicated studentized-deviate detection (RSD). The analysis results of the TNF-alpha-stimulated cells showed that RSD is simple and robust, and that the resulting statistical table is plausible.