Identification of putative biomarkers for leptomeningeal invasion in B-cell non-Hodgkin lymphoma by NMR metabolomics

Introduction

B-cell non-Hodgkin lymphoma (B-NHL) is the most common hematological malignancy and different genetic alterations are frequently detected in transformed B lymphocytes. Within this heterogeneous disease, certain aggressive subgroups have an increased risk of central nervous system (CNS) involvement at diagnosis and/or relapse, resulting in parenchymal or leptomeningeal infiltration (LI) in 5–15% of cases. The current sensitivity limitations of cerebrospinal fluid (CSF) cytology and contrast-enhanced MRI for CNS involvement, mainly at early stages, motivates the search for alternative diagnostic methods.

Objectives

Here we aim at using untargeted ¹H-NMR metabolomics to identify putative biomarkers for LI in B-NHL patients.

Methods

CSF and peripheral blood samples were obtained from B-NHL patients with a positive (n?=?7, LI group) or negative LI diagnostic (n?=?13, control group). For seven patients, CSF samples were collected during the course of intrathecal chemotherapy, making it possible to assess the patient´s response to treatment. ¹H-NMR spectra were acquired and statistical multivariate and univariate analysis were performed to identify significant alterations.

Results

Significant metabolite differences were found between LI and control groups in CSF, but not in serum. A predictive PLS-DA cross-validated model identified significant pool changes in glycine, alanine, pyruvate, acetylcarnitine, carnitine, and phenylalanine. Additionally, increments in protein signals were detected in the LI group. Significantly, the PLS-DA model predicted correctly all samples obtained from the group of patients in remission during LI treatment.

Conclusions

The results show that the CSF NMR-metabolomics approach is a promising complementary method in clinical diagnosis and treatment follow-up of LI in B-NHL patients.

     

Direct NOE refinement of biomolecular structures using 2D NMR data

Summary A set of computer programs called DINOSAUR has been developed, which allows the refinement of biomolecular structures directly from 2D NOE intensities. The NOE restraining potential implemented emphasises the weak intensities corresponding to larger distances which are more likely to determine the three-dimensional structure. An approximation based on a two-spin approximation is proposed for the gradient of the NOE intensities instead of the exact solution which is extremely time-consuming. The DINOSAUR routines have been implemented in various refinement programs (Distance bound Driven Dynamics, Molecular Dynamics and Energy Minimisation) and tested on an eight-residue model peptide.     

A new method to determine DNA sugar conformation from the joint use of 2D and 3D NMR data

The use of sugar restraints has been proven essential for assessing DNAstructures through molecular modeling studies. We present a new methodcombining 2D (COSY and NOESY) and 3D (NOESY-NOESY) experiments, whereconstraints on either the phase angles or the difference between phase anglesof two residues are obtained from comparison of 2D NOE H1-H4intensities and 3D NOE intensities containing the H1-H4transfer. All experiments lead to restraints that match, proving the validityof the method.     

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

Background  

Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) ¹H, projections of 2D ¹H, ¹H J-resolved (pJRES), and intact 2D J-resolved (JRES).     

How metabolomics can contribute to bio-processes: a proof of concept study for biomarkers discovery in the context of nitrogen-starved microalgae grown in photobioreactors

Microalgae appear to be one of the most promising sustainable resources as alternative crops for the production of renewable transport fuel. The exploitation of this bioresource requires, however, a fine monitoring of the culture conditions, for example by using more relevant control variables than usual macroscopic indicators (biomass or pigment estimation). In this proof of concept study, we propose to search potential biomarkers of progressive nitrogen regime culture conditions using an untargeted metabolomic approach based on LC-HRMS combined to a non-invasive analysis based on FTIR spectroscopy. One microalgae model was investigated i.e. Chlamydomonas reinhardtii to characterize the effect of progressive nitrogen regime in batch culture conditions on its metabolome. FTIR allowed assessing the intracellular macrometabolic perturbations, highlighting the over-accumulation of carbohydrates. LC-HRMS complemented the macromolecular information by revealing the dependence of microalgae metabotypes on nitrogen regime conditions tested for cells culture. Patterns of significantly modulated metabolites were also detected during those slight contrasted nitrogen regimes and interesting features were structurally elucidated. This included metabolites belonging to the pantothenate, branched chain and aromatic amino acids pathways. In the last step of this proof of concept study, amino acid targets proposed by metabolomic investigations were assessed on nitrogen-limited continuous culture on photobioreactors. This was performed to test the validity of proposed targets in real small-scale industrial production conditions. Results were very encouraging and suggested the possibility of using potentially relevant metabolites as intracellular biomarkers only (tryptophan) or as both intra and extracellular biomarkers (e.g. 2-methylbutyric acid and ketoleucine).     

Quantitating tissue specificity of human genes to facilitate biomarker discovery

We describe a method to identify candidate cancer biomarkers by analyzing numeric approximations of tissue specificity of human genes. These approximations were calculated by analyzing predicted tissue expression distributions of genes derived from mapping expressed sequence tags (ESTs) to the human genome sequence using a binary indexing algorithm. Tissue-specificity values facilitated high-throughput analysis of the human genes and enabled the identification of genes highly specific to different tissues. Tissue expression distributions for several genes were compared to estimates obtained from other public gene expression datasets and experimentally validated using quantitative RT-PCR on RNA isolated from several human tissues. Our results demonstrate that most human genes ( approximately 98%) are expressed in many tissues (low specificity), and only a small number of genes possess very specific tissue expression profiles. These genes comprise a rich dataset from which novel therapeutic targets and novel diagnostic serum biomarkers may be selected.     

Direct insight into insulin aggregation by 2D NMR complemented by PFGSE NMR

The aggregation of Zn(II)-bound and zinc-free human insulin was studied in solution using the H(beta)-CH(3) crosspeaks of threonine residues in 2D COSY, TOCSY, and NOESY NMR spectra which allow viewing of the oligomers in equilibrium. This is complemented by PFGSE measurements of the translational diffusion coefficient, D(i), used for monitoring the changes in equilibrium composition of aggregates on dilution of both insulins in physiological medium. The back calculation of the dilution isotherm allows establishing the association constants for oligomeric equilibria in solution and discussion of the models of association.     

The use of osmolytes to facilitate protein NMR spectroscopy

Summary A method of stabilizing folded proteins is described, which allows NMR studies under conditions where a protein would normally be unfolded. This enables stable proteins to be examined at elevated temperatures, or spectra recorded on samples that are insufficiently stable under normal conditions. Up to two molar perdeuterated glycine, a potent osmolyte, can be added to aqueous protein NMR samples without altering the folded three-dimensional structure or function of the protein. However, the stability of the folded form is dramatically increased. This is illustrated for the protein lysozyme at high temperature (348 K) where the structural integrity is destroyed in standard aqueous solution, but is retained in the osmolyte solution. We hope that the technique will be of value to those studying by NMR the structural biology of protein fragments and mutants, which are often of reduced stability compared with the original proteins.To whom correspondence should be addressed.     

Phenotyping COPD by 1H NMR metabolomics of exhaled breath condensate

Spirometry is used to establish the diagnosis of chronic obstructive pulmonary disease (COPD) and to assess disease progression, but it seems inadequate to characterize COPD phenotypes. Metabolomics has been introduced for molecular fingerprinting of biosamples in a variety of clinical disorders. The aim of the study was to establish whether exhaled breath condensate (EBC) in COPD features a distinct metabolic fingerprint, and to identify the metabolites that characterize the EBC profile in COPD. EBC was collected using a home-made glass condenser in 37 stable COPD patients, and 25 non-obstructed controls. Samples were analyzed using proton nuclear magnetic resonance spectroscopy (¹H NMR). Random forest was applied for both supervised and unsupervised learning, using spectral buckets as input variables. Metabolomics of EBC discriminated COPD patients from controls with an overall accuracy of 86 %. As compared to controls, EBC from COPD featured significantly lower (p < 0.05) levels of acetone, valine and lysine, and significantly higher (p < 0.05) levels of lactate, acetate, propionate, serine, proline, and tyrosine. Based on unsupervised analysis of NMR spectra, the COPD sample was split in three clusters, one of which had the highest prevalence of radiologic emphysema. NMR spectroscopy of EBC holds promise in COPD fingerprinting. It may prove valuable in outcome studies, and in assessing the efficacy of therapeutic interventions.     

Chemometric variance analysis of 1H NMR metabolomics data on the effects of oral rinse on saliva

Saliva is an easy to obtain bodily fluid that is specific to the oral environment. It can be used for metabolomic studies as it is representative of the overall wellbeing of an organism, as well as mouth health and bacterial flora. The metabolomic structure of saliva varies greatly depending on the bacteria present in the mouth as they produce a range of metabolites. In this study we have investigated the metabolomic profiles of human saliva that were obtained using ¹H NMR (nuclear magnetic resonance) analysis. 48 samples of saliva were collected from 16 healthy subjects over 3 days. Each sample was split in two and the first half treated with an oral rinse, while the second was left untreated as a control sample. The 96 ¹H NMR metabolomic profiles obtained in the dataset are affected by three factors, namely 16 subjects, 3 sampling days and 2 treatments. These three factors contribute to the total variation in the dataset. When analysing datasets from saliva using traditional methods such as PCA (principal component analysis), the overall variance is dominated by subjects’ contributions, and we cannot see trends that would highlight the effect of specific factors such as oral rinse. In order to identify these trends, we used methods such as MSCA (multilevel simultaneous component analysis) and ASCA (ANOVA simultaneous component analysis), that provide variance splits according to the experimental factors, so that we could look at the particular effect of treatment on saliva. The analysis of the treatment effect was enhanced, as it was isolated from the overall variance and assessed without confounding factors.

     

Using an NMR metabolomics approach to investigate the pathogenicity of amyloid-beta and alpha-synuclein

Introduction

The pathogenicity at differing points along the aggregation pathway of many fibril-forming proteins associated with neurodegenerative diseases is unclear. Understanding the effect of different aggregation states of these proteins on cellular processes is essential to enhance understanding of diseases and provide future options for diagnosis and therapeutic intervention.

Objectives

To establish a robust method to probe the metabolic changes of neuronal cells and use it to monitor cellular response to challenge with three amyloidogenic proteins associated with neurodegenerative diseases in different aggregation states.

Method

Neuroblastoma SH-SY5Y cells were employed to design a robust routine system to perform a statistically rigorous NMR metabolomics study into cellular effects of sub-toxic levels of alpha-synuclein, amyloid-beta 40 and amyloid-beta 42 in monomeric, oligomeric and fibrillar conformations.

Results

This investigation developed a rigorous model to monitor intracellular metabolic profiles of neuronal cells through combination of existing methods. This model revealed eight key metabolites that are altered when neuroblastoma cells are challenged with proteins in different aggregation states. Metabolic pathways associated with lipid metabolism, neurotransmission and adaptation to oxidative stress and inflammation are the predominant contributors to the cellular variance and intracellular metabolite levels. The observed metabolite changes for monomer and oligomer challenge may represent cellular effort to counteract the pathogenicity of the challenge, whereas fibrillar challenge is indicative of system shutdown. This implies that although markers of stress are more prevalent under oligomeric challenge the fibrillar response suggests a more toxic environment.

Conclusion

This approach is applicable to any cell type that can be cultured in a laboratory (primary or cell line) as a method of investigating how protein challenge affects signalling pathways, providing additional understanding as to the role of protein aggregation in neurodegenerative disease initiation and progression.

     

Structural investigations on betacyanin pigments by LC NMR and 2D NMR spectroscopy

Four betacyanin pigments were analysed by LC NMR and subjected to extensive NMR characterisation after isolation. Previously, low pH values were applied for NMR investigations of betalains resulting in rapid degradation of the purified substances thus preventing extensive NMR studies. Consequently, up to now only one single (13)C NMR spectrum of a betalain pigment, namely that of neobetanin (=14,15-dehydrobetanin), was available. Because of its sufficient stability under highly acidic conditions otherwise detrimental for betacyanins, this pigment remained an exemption. Since betalains are most stable in the pH range of 5-7, a new solvent system has been developed allowing improved data acquisition through improved pigment stability at near neutral pH. Thus, not only (1)H, but for the first time also partial (13)C data of betanin, isobetanin, phyllocactin and hylocerenin isolated from red-purple pitaya [Hylocereus polyrhizus (Weber) Britton & Rose, Cactaceae] could be indirectly obtained by gHSQC- and gHMQC-NMR experiments.     

Pharmacogenetics meets metabolomics: discovery of tryptophan as a new endogenous OCT2 substrate related to metformin disposition

Genetic polymorphisms of the organic cation transporter 2 (OCT2), encoded by SLC22A2, have been investigated in association with metformin disposition. A functional decrease in transport function has been shown to be associated with the OCT2 variants. Using metabolomics, our study aims at a comprehensive monitoring of primary metabolite changes in order to understand biochemical alteration associated with OCT2 polymorphisms and discovery of potential endogenous metabolites related to the genetic variation of OCT2. Using GC-TOF MS based metabolite profiling, clear clustering of samples was observed in Partial Least Square Discriminant Analysis, showing that metabolic profiles were linked to the genetic variants of OCT2. Tryptophan and uridine presented the most significant alteration in SLC22A2-808TT homozygous and the SLC22A2-808G>T heterozygous variants relative to the reference. Particularly tryptophan showed gene-dose effects of transporter activity according to OCT2 genotypes and the greatest linear association with the pharmacokinetic parameters (Cl(renal), Cl(sec), Cl/F/kg, and Vd/F/kg) of metformin. An inhibition assay demonstrated the inhibitory effect of tryptophan on the uptake of 1-methyl-4-phenyl pyrinidium in a concentration dependent manner and subsequent uptake experiment revealed differential tryptophan-uptake rate in the oocytes expressing OCT2 reference and variant (808G>T). Our results collectively indicate tryptophan can serve as one of the endogenous substrate for the OCT2 as well as a biomarker candidate indicating the variability of the transport activity of OCT2.     

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.     

A novel structure observed in d-GAATTCCCGAATTC by 2D NMR

Sequential resonance assignments of the non exchangeable base and sugar protons in d-GAATTCCCGAATTC have been obtained using two dimensional NMR experiments at 500 MHz. The chemical shifts and the NOEs have been used to determine the structure in the base-pair mismatch region which is located in the central portion of the molecule. It is observed that the molecule adopts a novel unsymmetrical loop structure in this section which is characterised by sugar geometries which are significantly different compared to the rest of the molecular. The base-paired portion of the molecule conforms to a right handed B-DNA type of structure.     

The application of micro-coil NMR probe technology to metabolomics of urine and serum

Increasing the sensitivity and throughput of NMR-based metabolomics is critical for the continued growth of this field. In this paper the application of micro-coil NMR probe technology was evaluated for this purpose. The most commonly used biofluids in metabolomics are urine and serum. In this study we examine different sample limited conditions and compare the detection sensitivity of the micro-coil with a standard 5?mm NMR probe. Sample concentration is evaluated as a means to leverage the greatly improved mass sensitivity of the micro-coil probes. With very small sample volumes, the sensitivity of the micro-coil probe does indeed provide a significant advantage over the standard probe. Concentrating the samples does improve the signal detection, but the benefits do not follow the expected linear increase and are both matrix and metabolite specific. Absolute quantitation will be affected by concentration, but an analysis of relative concentrations is still possible. The choice of the micro-coil probe over a standard tube based probe will depend upon a number of factors including number of samples and initial volume but this study demonstrates the feasibility of high-throughput metabolomics with the micro-probe platform.     

Analysis of MALDI-TOF mass spectrometry data for discovery of peptide and glycan biomarkers of hepatocellular carcinoma

This paper presents computational methods to analyze MALDI-TOF mass spectrometry data for quantitative comparison of peptides and glycans in serum. The methods are applied to identify candidate biomarkers in serum samples of 203 participants from Egypt; 73 hepatocellular carcinoma (HCC) cases, 52 patients with chronic liver disease (CLD) consisting of cirrhosis and fibrosis cases, and 78 population controls. Two complementary sample preparation methods were applied prior to generating mass spectra: (1) low molecular weight (LMW) enrichment of each serum sample was carried out for MALDI-TOF quantification of peptides, and (2) glycans were enzymatically released from proteins in each serum sample and permethylated for MALDI-TOF quantification of glycans. A peak selection algorithm was applied to identify the most useful peptide and glycan peaks for accurate detection of HCC cases from high-risk population of patients with CLD. In addition to global peaks selected by the whole population based approach, where identically labeled patients are treated as a single group, subgroup-specific peaks were identified by searching for peaks that are differentially abundant in a subgroup of patients only. The peak selection process was preceded by peak screening, where we eliminated peaks that have significant association with covariates such as age, gender, and viral infection based on the peptide and glycan spectra from population controls. The performance of the selected peptide and glycan peaks was evaluated in terms of their ability in detecting HCC cases from patients with CLD in a blinded validation set and through the cross-validation method. Finally, we investigated the possibility of using both peptides and glycans in a panel to enhance the diagnostic capability of these candidate markers. Further evaluation is needed to examine the potential clinical utility of the candidate peptide and glycan markers identified in this study.     

Application of 2D and 3D NMR experiments to the conformational study of a diantennary oligosaccharide

Summary By the application of homonuclear 3D NOE-HOHAHA and heteronuclear 3D HMQC-NOE experiments in studies of complex oligosaccharides. NOEs can be investigated which are hidden in conventional 2D NOE spectra. In the 3D NOE-HOHAHA spectrum ₃ cross sections were considered to be the most suitable for assignment of NOEs. Alternatively, these cross sections could be measured separately in selective 2D HOHAHA-NOE spectroscopy. The advantages and limitations of the 2D alternative are compared with those of the 3D NOE-HOHAHA approach. In 3D HMQC-NOE spectroscopy the larger chemical shift displacement of the carbon spectrum with respect to the proton spectrum can be used to unmask NOEs hidden in the bulk region. If the extra proton dimension is not needed, 2D HMQC-NOE is a good alternative.The suitability of 2D and 3D NOE-HOHAHA and HMQC-NOE experiments for the estimation of proton-proton distances is demonstrated by comparing the results of these experiments on a diantennary asparagine-linked oligosaccharide with those of a conventional 2D NOE experiment. NOEs identified in the 2D and 3D NOE-HOHAHA as well as HMQC-NOE experiments, so far not identified or not quantified in 2D NOE experiments, are discussed in relation to each glycosidic linkage. The flexibility of the Man(1-3)Man linkage is demonstrated, confirming the existence of an ensemble of conformations for this linkage.     

Surface fitting of 2D diffusion-edited 1H NMR spectroscopy data for the characterisation of human plasma lipoproteins

Determining the concentration and size of lipoprotein complexes is very important due to their role in cardiovascular diseases and metabolic disorders. However, standard methods for lipoprotein fractionation are manual and time consuming and cannot be used as standard diagnostic tools. Because different subclasses of lipoproteins have different radii and, hence, different diffusion velocities, we propose a fast and reliable method that uses 2D diffusion-edited ¹H NMR spectroscopy to acquire a set of 2D spectra of plasma samples, followed by a surface fitting algorithm based on Lorentzian functions to estimate the sizes and the relative proportions of different lipoprotein subclasses. We were able to demonstrate that the derived sizes and positions related to the Lorentzian functions follow an exponential relationship for normolipidaemic and dislipaemic samples with coefficients of determination (r ²) of 0.85 and 0.81, respectively. Moreover, we found a linear relationship between the width and size of the Lorentzian functions for normolipidaemic samples (r ² = 0.88) while for dislipaemic samples this relation was nonlinear (r ² = 0.62). Dividing our samples set into four different lipoprotein profiles (normal lipid values, low HDL/LDL ratio, high triglycerides values and both risk factors) and using principal component analysis (PCA) followed by multivariate analysis of variance (MANOVA), our method was able to statistically discriminate between those groups, with p-values of 0.0016, 0.0006, <1e⁻⁴ and 0.0035, respectively. These parameters are characteristic and indicative of different lipoprotein profiles and can be used to distinguish between normolipidaemic, hypercholesterolaemic, hypertriglyceridaemic and chylomicronaemic profiles.     

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.