Predictive Performance of 1 H-NMR Metabolomics-Derived Biomarkers of Bacterial Vaginosis.

ABSTRACT: 1 H-NMR metabolomics-derived biomarkers maltose, acetate, formate, and lactate have excellent potential as predictive biomarkers for bacterial vaginosis with an area under curve of 0.97 (95% confidence interval, 0.88-1.00), sensitivity of 0.90, and specificity of 0.95.

Gobind: an inspiring enigma.

Gobind Khorana's distinguished career spanned nearly six decades (1952-2011). His work resulted in remarkable achievements starting with the complicated synthesis of coenzyme A. He then pioneered the synthesis of DNA oligonucleotides, which enabled him to crack the genetic code. Using this experience, he ventured to accomplish the first complete synthesis of a gene. Not satisfied with elucidating the function of bacteriorhodopsin, Gobind took up another greater challenge, that of spearheading studies on visual rhodopsin, its mechanism of activation, and the consequent signal transduction pathway. This Editorial acts to introduce the articles appearing in this Issue Focus dedicated to celebrating the 100th anniversary of the year of his birth.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research.

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.

Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.

NMR based metabolic profiling of patients with potential celiac disease elucidating early biochemical changes of gluten-sensitivity: A pilot study.

BACKGROUND: The patients with positive celiac disease (CeD) specific serology, but no evidence of intestinal inflammation are defined as potential celiac disease (PCeD) patients. About one-third of PCeD patients develop intestinal inflammation over time. The present study investigated the metabolome of small intestinal biopsies, blood plasma, and urine of patients with PCeD to understand the biochemical changes underlying the CeD. METHODS: The metabolic profiles of small intestinal biopsies, blood plasma, and urine of patients with PCeD (n = 7) were compared with CeD (n = 64) and controls (n = 15) [disease controls (DC) and healthy controls (HC)] using 1H NMR spectroscopy. RESULTS: The intestinal mucosa of PCeD showed lower levels of histidine, glycine, tyrosine, and tryptophan compared to DC. Altered levels of 6 metabolites (glucose, acetate, acetoacetate, ß-hydroxybutyrate, pyruvate, arginine) in blood plasma and two metabolites (succinate and aminohippurate) in urine were observed in PCeD compared to HC. The PLS-DA model built on the concentration of blood plasma showed separate clustering for PCeD and CeD patients. CONCLUSION: Altered metabolic profile of PCeD suggested that gluten intolerance was evident at the metabolic level before the intestinal damage. Altered energy metabolism and lower cytoprotective activity (histidine, glycine, arginine) indicated vulnerability to develop intestinal inflammation in PCeD over time. Our study may provide an insight into early biochemical processes of the progression of PCeD to CeD.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism.

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer.

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Differentiation between sepsis survivors and sepsis non-survivors through blood serum metabolomics: A proton nuclear magnetic resonance spectroscopy (NMR) study.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is a subset of sepsis with underlying circulatory cellular and metabolic abnormalities associated with higher mortality rates. However, a detailed understanding of sepsis is still limited. The present study reports the differences in the metabolic profile of serum samples of patients with sepsis compared to healthy controls using Nuclear Magnetic Resonance (NMR) spectroscopy. The study also compares the NMR metabolomics on day zero of admission among sepsis survivors (those who survived till day seven) and sepsis non-survivors (those who succumbed on day zero). Furthermore, the different metabolites in serum were analysed by univariate and multivariate analysis, ROC analysis, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. Metabolites with VIP score (>1·0) were considered as potential biomarker/s to discriminate sepsis survivors from non- survivors at day zero. Data showed that phenylalanine was significantly higher in sepsis patients compared to healthy controls, whereas isoleucine, valine and histidine were significantly lower in sepsis patients compared to healthy controls. Also, non-survivors had higher serum levels of creatine, phosphocreatine, choline, betaine, tyrosine, histidine and phenylalanine concentrations than survivors. These findings suggest that the metabolic alterations at day zero may predict the survival of patients with sepsis. The significant differences seen in metabolites concentration of amino acids, phospholipids and creatine may be used as early prognostic markers to discriminate non-survivors from survivors of sepsis patients at day zero. Our findings indicate that the metabolite alterations are associated with the progression of the disease.

Sustainable low-field cardiovascular magnetic resonance in changing healthcare systems.

Cardiovascular disease continues to be a major burden facing healthcare systems worldwide. In the developed world, cardiovascular magnetic resonance (CMR) is a well-established non-invasive imaging modality in the diagnosis of cardiovascular disease. However, there is significant global inequality in availability and access to CMR due to its high cost, technical demands as well as existing disparities in healthcare and technical infrastructures across high-income and low-income countries. Recent renewed interest in low-field CMR has been spurred by the clinical need to provide sustainable imaging technology capable of yielding diagnosticquality images whilst also being tailored to the local populations and healthcare ecosystems. This review aims to evaluate the technical, practical and cost considerations of low field CMR whilst also exploring the key barriers to implementing sustainable MRI in both the developing and developed world.

A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction.

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on both the image and the Fourier domain. For a single-coil acquisition, we introduce deep cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity (DF) units. Secondly, we improve the structure recovery of DC-RSN for T2 weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a sequence with short acquisition time. T1 assistance is provided to DC-RSN through a gradient of log feature (GOLF) fusion. Furthermore, we propose perceptual refinement network (PRN) to refine the reconstructions for better visual information fidelity (VIF), a metric highly correlated to radiologist's opinion on the image quality. Lastly, for multi-coil acquisition, we propose variable splitting RSN (VS-RSN), a deep cascade of blocks, each block containing RSN, multi-coil DF unit, and a weighted average module. We extensively validate our models DC-RSN and VS-RSN for single-coil and multi-coil acquisitions and report the state-of-the-art performance. We obtain a SSIM of 0.768, 0.923, and 0.878 for knee single-coil-4x, multi-coil-4x, and multi-coil-8x in fastMRI, respectively. We also conduct experiments to demonstrate the efficacy of GOLF based T1 assistance and PRN.

Visual Cortex Alterations in Early and Late Blind Subjects During Tactile Perception.

Involvement of visual cortex varies during tactile perception tasks in early blind (EB) and late blind (LB) human subjects. This study explored differences in sensory motor networks associated with tactile task in EB and LB subjects and between children and adolescents. A total of 40 EB subjects, 40 LB subjects, and 30 sighted controls were recruited in two subgroups: children (6-12 years) and adolescents (13-19 years). Data were acquired using a 3T MR scanner. Analyses of blood oxygen level dependent (BOLD), functional connectivity (FC), correlation, and post hoc test for multiple comparisons were carried out. Difference in BOLD activity was observed in EB and LB groups in visual cortex during tactile perception, with increased FC of visual with dorsal attention and sensory motor networks in EB. EB adolescents exhibited increased connectivity with default mode and salience networks when compared with LB. Functional results correlated with duration of training, suggestive of better performance in EB. Alteration in sensory and visual networks in EB and LB correlated with duration of tactile training. Age of onset of blindness has an effect in cross-modal reorganization of visual cortex in EB and multimodal in LB in children and adolescents.

MR spectroscopy in breast cancer metabolomics.

Breast cancer poses a significant health care challenge worldwide requiring early detection and effective treatment strategies for better patient outcome. A deeper understanding of the breast cancer biology and metabolism may help developing better diagnostic and therapeutic approaches. Metabolomic studies give a comprehensive analysis of small molecule metabolites present in human tissues in vivo. The changes in the level of these metabolites provide information on the complex mechanism of the development of the disease and its progression. Metabolomic approach using analytical techniques such as magnetic resonance spectroscopy (MRS) has evolved as an important tool for identifying clinically relevant metabolic biomarkers. The metabolic characterization of breast lesions using in-vivo MRS has shown that malignant breast tissues contain elevated levels of choline containing compounds (tCho), suggesting rapid proliferation of cancer cells and alterations in membrane metabolism. Also, tCho has been identified as one of the important biomarkers that help to enhance the diagnostic accuracy of dynamic contrast enhanced magnetic resonance imaging and also for monitoring treatment response. Further, metabolome of malignant tissues can be studied using ex vivo and in vitro MRS at high magnetic fields. This provided the advantage of detection of a large number of compounds that facilitated more comprehensive insight into the altered metabolic pathways associated with the cancer development and progression and also in identification of several metabolites as potential biomarkers. This article briefly reviews the role of MRS based metabolic profiling in the discovery of biomarkers and understanding of the altered metabolism in breast cancer.

Metabolism of prostate cancer by magnetic resonance spectroscopy (MRS).

Understanding the metabolism of prostate cancer (PCa) is important for developing better diagnostic approaches and also for exploring new therapeutic targets. Magnetic resonance spectroscopy (MRS) techniques have been shown to be useful in the detection and quantification of metabolites. PCa illustrates metabolic phenotype, showing lower levels of citrate (Cit), a key metabolite of oxidative phosphorylation and alteration in several metabolic pathways to sustain tumor growth. Recently, dynamic nuclear polarization (DNP) studies have documented high rates of glycolysis (Warburg phenomenon) in PCa. High-throughput metabolic profiling strategies using MRS on variety of samples including intact tissues, biofluids like prostatic fluid, seminal fluid, blood plasma/sera, and urine have also played a vital role in understanding the abnormal metabolic activity of PCa patients. The enhanced analytical potential of these techniques in the detection and quantification of a large number of metabolites provides an in-depth understanding of metabolic rewiring associated with the tumorigenesis. Metabolomics analysis offers dual advantages of identification of diagnostic and predictive biomarkers as well as in understanding the altered metabolic pathways which can be targeted for inhibiting the cancer progression. This review briefly describes the potential applications of in vivo 1H MRS, high-resolution magic angle spinning spectroscopy (HRMAS) and in vitro MRS methods in understanding the metabolic changes of PCa and its usefulness in the management of PCa patients.

Abnormalities in metabolic pathways in celiac disease investigated by the metabolic profiling of small intestinal mucosa, blood plasma and urine by NMR spectroscopy.

Celiac disease (CeD) is an autoimmune enteropathy caused by gluten intake in genetically predisposed individuals. We investigated the metabolism of CeD by metabolic profiling of intestinal mucosa, blood plasma and urine using NMR spectroscopy and multivariate analysis. The metabolic profile of the small intestinal mucosa was compared between patients with CeD (n = 64) and disease controls (DCs, n = 30). The blood plasma and urinary metabolomes of CeD patients were compared with healthy controls (HCs, n = 39). Twelve metabolites (proline (Pro), arginine (Arg), glycine (Gly), histidine (His), glutamate (Glu), aspartate, tryptophan (Trp), fumarate, formate, succinate (Succ), glycerophosphocholine (GPC) and allantoin (Alln)) of intestinal mucosa differentiated CeD from controls. The metabolome of blood plasma with 18 metabolites (Pro, Arg, Gly, alanine, Glu, glutamine, glucose (Glc), lactate (Lac), acetate (Ace), acetoacetate (AcAc), ß-hydroxybutyrate (ß-OHB), pyruvate (Pyr), Succ, citrate (Cit), choline (Cho), creatine (Cr), phosphocreatine (PCr) and creatinine) and 9 metabolites of urine (Pro, Trp, ß-OHB, Pyr, Succ, N-methylnicotinamide (NMN), aminohippurate (AHA), indoxyl sulfate (IS) and Alln) distinguished CeD from HCs. Our data demonstrated changes in nine metabolic pathways. The altered metabolites were associated with increased oxidative stress (Alln), impaired healing and repair mechanisms (Pro, Arg), compromised anti-inflammatory and cytoprotective processes (Gly, His, NMN), altered energy metabolism (Glc, Lac, ß-OHB, Ace, AcAc, Pyr, Succ, Cit, Cho, Cr and PCr), impaired membrane metabolism (GPC and Cho) and intestinal dysbiosis (AHA and IS). An orthogonal partial least square discriminant analysis model provided clear differentiation between patients with CeD and controls in all three specimens. A classification model built by combining the distinguishing metabolites of blood plasma and urine samples gave an AUC of 0.99 with 97.7% sensitivity, 93.3% specificity and a predictive accuracy of 95.1%, which was higher than for the models built separately using small intestinal mucosa, blood plasma and urine. In conclusion, a panel of metabolic biomarkers in intestinal biopsies, plasma and urine samples has potential to differentiate CeD from controls and may complement traditional tests to improve the diagnosis of CeD.

1 H NMR metabolomic profiling elucidated attenuation of neurometabolic alterations by lercanidipine in MCAo model in rats.

OBJECTIVES: Stroke is a leading cause of death and disability worldwide with limited therapeutic interventions. The current study explored proton nuclear magnetic resonance spectroscopy (1 H NMR)-based metabolomic approach to elucidate the effect of lercanidipine on neurometabolic alterations in transient model of ischaemic stroke in rats. METHODS: In the present investigation, male Wistar rats were subjected to middle cerebral artery occlusion (MCAo) for 2 h followed by reperfusion using intraluminal filament method. Rats were randomly divided into three groups as vehicle-treated sham control, vehicle-treated MCAo control and lercanidipine-treated MCAo. Vehicle or lercanidipine (0.5 mg/kg, i.p.) was administered 120 min post-reperfusion. The rat brain cortex tissues were isolated 24 h post-MCAo and were investigated by 1 H NMR spectroscopy through perchloric extraction method. KEY FINDINGS: A total of 23 metabolites were altered significantly after cerebral ischaemic-reperfusion injury in MCAo control as compared to sham control rats. Lercanidipine significantly reduced the levels of valine, alanine, lactate, acetate and tyrosine, while N-acetylaspartate, glutamate, glutamine, aspartate, creatine/phosphocreatine, choline, glycerophosphorylcholine, taurine, myo-inositol and adenosine di-phosphate were elevated as compared to MCAo control. CONCLUSIONS: Present study illustrates effect of lercanidipine on neurometabolic alterations which might be mediated through its antioxidant, anti-inflammatory, vasodilatory and anti-apoptotic property in MCAo model of stroke.