Kirill Veselkov

Human metabolic phenotype diversity and its association with diet and blood pressure

Metabolic phenotypes are the products of interactions among a variety of factors—dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40–59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10-16), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.

Intraoperative Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry

A mass spectrometric approach was developed for intraoperative identification of cancerous tissue, in near–real-time. Diagnosing the Masses One of the best options for curing cancer is surgery. Yet, surgeons can leave cancerous tissue behind by not seeing the “tumor margins”—or edges of the tumor—clearly. If a surgeon isn’t sure whether tissue is normal or cancerous, the tissue is sent to a pathologist for testing. During this time (20 to 30 min), the patient remains under anesthesia, and, quite often, additional samples are required. To ensure that all malignant tissue is removed in the operating room, Balog and colleagues developed a mass spectrometry–based approach that identifies cancer during surgery. After analyzing ex vivo samples of cancerous, healthy, and benign/inflammatory tissue with rapid evaporative ionization mass spectrometry (REIMS), the authors created a database of the nearly 3000 tissue-specific mass spectra. These spectra were unique for each cancer type, with lipids such as phosphatidylcholine and phosphotidylinositol showing different ratios. Using these ratios, Balog et al. were even able to identify the origin of metastatic tumors ex vivo. To adapt this technology for use in vivo, during surgery, the authors created the “intelligent knife” (iKnife), which samples surgical smoke for mass spectrometric analysis. More than 800 spectra were acquired with the iKnife from 81 patients. These spectra, when matched against the previously created database, confirmed the results of normal histology, with low rates of false-positive and false-negative readouts. This first-in-human demonstration shows that the iKnife technology is ready for widespread use in the operating room to improve the accuracy of surgical intervention in cancer. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near–real-time characterization of human tissue in vivo by analysis of the aerosol (“smoke”) released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

Recursive Segment-Wise Peak Alignment of Biological 1H NMR Spectra for Improved Metabolic Biomarker Recovery

Chemical shift variation in small-molecule (1)H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. We present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple (1)H NMR spectra used in metabonomic studies. The method refines a segmentation of reference and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. We also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 (1)H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. We demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatographic profiles and MS data.

Urinary Metabolic Phenotyping Differentiates Children with Autism from Their Unaffected Siblings and Age-Matched Controls

Autism is an early onset developmental disorder with a severe life-long impact on behavior and social functioning that has associated metabolic abnormalities. The urinary metabolic phenotypes of individuals (age range=3-9 years old) diagnosed with autism using the DSM-IV-TR criteria (n = 39; male = 35; female = 4), together with their nonautistic siblings (n = 28; male = 14; female = 14) and age-matched healthy volunteers (n = 34, male = 17; female = 17) have been characterized for the first time using (1)H NMR spectroscopy and pattern recognition methods. Novel findings associated with alterations in nicotinic acid metabolism within autistic individuals showing increased urinary excretion of N-methyl-2-pyridone-5-carboxamide, N-methyl nicotinic acid, and N-methyl nicotinamide indicate a perturbation in the tryptophan-nicotinic acid metabolic pathway. Multivariate statistical analysis indicated urinary patterns of the free amino acids, glutamate and taurine were significantly different between groups with the autistic children showing higher levels of urinary taurine and a lower level of urinary glutamate, indicating perturbation in sulfur and amino acid metabolism in these children. Additionally, metabolic phenotype (metabotype) differences were observed between autistic and control children, which were associated with perturbations in the relative patterns of urinary mammalian-microbial cometabolites including dimethylamine, hippurate, and phenyacetylglutamine. These biochemical changes are consistent with some of the known abnormalities of gut microbiota found in autistic individuals and the associated gastrointestinal dysfunction and may be of value in monitoring the success of therapeutic interventions.

Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Significance Mass spectrometry imaging (MSI) technology represents a highly promising approach in cancer research. Here, we outline current roadblocks in translational MSI and introduce a comprehensive workflow designed to address current methodological limitations. An integrated bioinformatics platform is presented that allows intuitive histology-directed interrogation of MSI datasets. We show that this strategy permits the analysis of multivariate molecular signatures with direct correlation to morphological regions of interest, which can offer new insights into how different tumor microenvironmental populations interact with one another and generate novel region-of-interest specific biomarkers and therapeutic targets. Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.

Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry.

Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis.

1H NMR Study on the Short- and Long-Term Impact of Two Training Programs of Sprint Running on the Metabolic Fingerprint of Human Serum

Metabonomics is an established strategy in the exploration of the effects of various stimuli on the metabolic fingerprint of biofluids. Here, we present an application of (1)H NMR-based metabonomics on the field of exercise biochemistry. Fourteen men were assigned to either of two training programs, which lasted 8 weeks and involved sets of 80-m maximal runs separated by either 10 s or 1 min of rest. Analysis of pre- and postexercise serum samples, both at the beginning and end of training, by (1)H NMR spectroscopy and subsequent multivariate statistical techniques revealed alterations in the levels of 18 metabolites. Validated O-PLS models could classify the samples in regard to exercise, the separation being mainly due to lactate, pyruvate, alanine, leucine, valine, isoleucine, arginine/lysine, glycoprotein acetyls, and an unidentified metabolite resonating at 8.17 ppm. Samples were also classified safely with respect to training, the separation being mainly due to lactate, pyruvate, methylguanidine, citrate, glucose, valine, taurine, trimethylamine N-oxide, choline-containing compounds, histidines, acetoacetate/acetone, glycoprotein acetyls, and lipids. Samples could not be classified according to the duration of the rest interval between sprints. Our findings underline the power of metabonomics to offer new insights into the short- and long-term impact of exercise on metabolism.

Rapid diagnosis and staging of colorectal cancer via high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy of intact tissue biopsies.

Objective:To develop novel metabolite-based models for diagnosis and staging in colorectal cancer (CRC) using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy. Background:Previous studies have demonstrated that cancer cells harbor unique metabolic characteristics relative to healthy counterparts. This study sought to characterize metabolic properties in CRC using HR-MAS NMR spectroscopy. Methods:Between November 2010 and January 2012, 44 consecutive patients with confirmed CRC were recruited to a prospective observational study. Fresh tissue samples were obtained from center of tumor and 5 cm from tumor margin from surgical resection specimens. Samples were run in duplicate where tissue volume permitted to compensate for anticipated sample heterogeneity. Samples were subjected to HR-MAS NMR spectroscopic profiling and acquired spectral data were imported into SIMCA and MATLAB statistical software packages for unsupervised and supervised multivariate analysis. Results:A total of 171 spectra were acquired (center of tumor, n = 88; 5 cm from tumor margin, n = 83). Cancer tissue contained significantly increased levels of lactate (P < 0.005), taurine (P < 0.005), and isoglutamine (P < 0.005) and decreased levels of lipids/triglycerides (P < 0.005) relative to healthy mucosa (R2Y = 0.94; Q2Y = 0.72; area under the curve, 0.98). Colon cancer samples (n = 49) contained higher levels of acetate (P < 0.005) and arginine (P < 0.005) and lower levels of lactate (P < 0.005) relative to rectal cancer samples (n = 39). In addition unique metabolic profiles were observed for tumors of differing T-stage. Conclusions:HR-MAS NMR profiling demonstrates cancer-specific metabolic signatures in CRC and reveals metabolic differences between colonic and rectal cancers. In addition, this approach reveals that tumor metabolism undergoes modification during local tumor advancement, offering potential in future staging and therapeutic approaches.

Characterization and Identification of Clinically Relevant Microorganisms Using Rapid Evaporative Ionization Mass Spectrometry

Rapid evaporative ionization mass spectrometry (REIMS) was investigated for its suitability as a general identification system for bacteria and fungi. Strains of 28 clinically relevant bacterial species were analyzed in negative ion mode, and corresponding data was subjected to unsupervised and supervised multivariate statistical analyses. The created supervised model yielded correct cross-validation results of 95.9%, 97.8%, and 100% on species, genus, and Gram-stain level, respectively. These results were not affected by the resolution of the mass spectral data. Blind identification tests were performed for strains cultured on different culture media and analyzed using different instrumental platforms which led to 97.8-100% correct identification. Seven different Escherichia coli strains were subjected to different culture conditions and were distinguishable with 88% accuracy. In addition, the technique proved suitable to distinguish five pathogenic Candida species with 98.8% accuracy without any further modification to the experimental workflow. These results prove that REIMS is sufficiently specific to serve as a culture condition-independent tool for the identification and characterization of microorganisms.