Radoslav Goldman

Profiling of Human Serum Glycans Associated with Liver Cancer and Cirrhosis by IMS-MS

Aberrant glycosylation of human glycoproteins is related to various physiological states, including the onset of diseases such as cancer. Consequently, the search for glycans that could be markers of diseases or targets of therapeutic drugs has been intensive. Here, we describe a high-throughput ion mobility spectrometry/mass spectrometry analysis of N-linked glycans from human serum. Distributions of glycans are assigned according to their m/z values, while ion mobility distributions provide information about glycan conformational and isomeric composition. Statistical analysis of data from 22 apparently healthy control patients and 39 individuals with known diseases (20 with cirrhosis of the liver and 19 with liver cancer) shows that ion mobility distributions for individual m/z ions appear to be sufficient to distinguish patients with liver cancer or cirrhosis. Measurements of glycan conformational and isomeric distributions by IMS-MS may provide insight that is valuable for detecting and characterizing disease states.

Detection of Hepatocellular Carcinoma Using Glycomic Analysis

Purpose: Hepatocellular carcinoma (HCC) represents an increasing health problem in the United States. Serum α-fetoprotein, the currently used clinical marker, is elevated in only ∼60% of HCC patients; therefore, the identification of additional markers is expected to have significant public health impact. The objective of our study was to quantitatively assess N-glycans originating from serum glycoproteins as alternative markers for the detection of HCC. Experimental Design: We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for quantitative comparison of 83 N-glycans in serum samples of 202 participants (73 HCC cases, 77 age- and gender-matched cancer-free controls, and 52 patients with chronic liver disease). N-glycans were enzymatically released from serum glycoproteins and permethylated before mass spectrometric quantification. Results: The abundance of 57 N-glycans was significantly altered in HCC patients compared with controls. The sensitivity of six individual glycans evaluated for separation of HCC cases from population controls ranged from 73% to 90%, and the specificity ranged from 36% to 91%. A combination of three selected N-glycans was sufficient to classify HCC with 90% sensitivity and 89% specificity in an independent validation set of patients with chronic liver disease. The three N-glycans remained associated with HCC after adjustment for chronic viral infection and other known covariates, whereas the other glycans increased significantly at earlier stages of the progression of chronic viral infection to HCC. Conclusion: A set of three identified N-glycans is sufficient for the detection of HCC with 90% prediction accuracy in a population with high rates of hepatitis C viral infection. Further evaluation of a wider clinical utility of these candidate markers is warranted.

Peak selection from MALDI-TOF mass spectra using ant colony optimization

MOTIVATION Due to the large number of peaks in mass spectra of low-molecular-weight (LMW) enriched sera, a systematic method is needed to select a parsimonious set of peaks to facilitate biomarker identification. We present computational methods for matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectral data preprocessing and peak selection. In particular, we propose a novel method that combines ant colony optimization (ACO) with support vector machines (SVM) to select a small set of useful peaks. RESULTS The proposed hybrid ACO-SVM algorithm selected a panel of eight peaks out of 228 candidate peaks from MALDI-TOF spectra of LMW enriched sera. An SVM classifier built with these peaks achieved 94% sensitivity and 100% specificity in distinguishing hepatocellular carcinoma from cirrhosis in a blind validation set of 69 samples. Area under the receiver operating characteristic (ROC) curve was 0.996. The classification capability of these peaks is compared with those selected by the SVM-recursive feature elimination method. AVAILABILITY Supplementary material and MATLAB scripts to implement the methods described in this article are available at http://microarray.georgetown.edu/web/files/bioinf.htm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

Analysis of mass spectral serum profiles for biomarker selection

MOTIVATION Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection. RESULTS The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum. AVAILABILITY MATLAB scripts to implement the methods described in this paper are available from the HWRs lab website http://lombardi.georgetown.edu/labpage

Interlaboratory Study on Differential Analysis of Protein Glycosylation by Mass Spectrometry: the ABRF Glycoprotein Research Multi-Institutional Study 2012

One of the principal goals of glycoprotein research is to correlate glycan structure and function. Such correlation is necessary in order for one to understand the mechanisms whereby glycoprotein structure elaborates the functions of myriad proteins. The accurate comparison of glycoforms and quantification of glycosites are essential steps in this direction. Mass spectrometry has emerged as a powerful analytical technique in the field of glycoprotein characterization. Its sensitivity, high dynamic range, and mass accuracy provide both quantitative and sequence/structural information. As part of the 2012 ABRF Glycoprotein Research Group study, we explored the use of mass spectrometry and ancillary methodologies to characterize the glycoforms of two sources of human prostate specific antigen (PSA). PSA is used as a tumor marker for prostate cancer, with increasing blood levels used to distinguish between normal and cancer states. The glycans on PSA are believed to be biantennary N-linked, and it has been observed that prostate cancer tissues and cell lines contain more antennae than their benign counterparts. Thus, the ability to quantify differences in glycosylation associated with cancer has the potential to positively impact the use of PSA as a biomarker. We studied standard peptide-based proteomics/glycomics methodologies, including LC-MS/MS for peptide/glycopeptide sequencing and label-free approaches for differential quantification. We performed an interlaboratory study to determine the ability of different laboratories to correctly characterize the differences between glycoforms from two different sources using mass spectrometry methods. We used clustering analysis and ancillary statistical data treatment on the data sets submitted by participating laboratories to obtain a consensus of the glycoforms and abundances. The results demonstrate the relative strengths and weaknesses of top-down glycoproteomics, bottom-up glycoproteomics, and glycomics methods.

Semi-Automated Identification of N-Glycopeptides by Hydrophilic Interaction Chromatography, nano-Reverse-Phase LC-MS/MS, and Glycan Database Search

Glycoproteins fulfill many indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. Improved analytical methods are needed to allow a complete characterization of this complex and common post-translational modification. In this study, we present a workflow for the analysis of the microheterogeneity of N-glycoproteins that couples hydrophilic interaction and nanoreverse-phase C18 chromatography to tandem QTOF mass spectrometric analysis. A glycan database search program, GlycoPeptideSearch, was developed to match N-glycopeptide MS/MS spectra with the glycopeptides comprised of a glycan drawn from the GlycomeDB glycan structure database and a peptide from a user-specified set of potentially glycosylated peptides. Application of the workflow to human haptoglobin and hemopexin, two microheterogeneous N-glycoproteins, identified a total of 57 distinct site-specific glycoforms in the case of haptoglobin and 14 site-specific glycoforms of hemopexin. Using glycan oxonium ions and peptide-characteristic glycopeptide fragment ions and by collapsing topologically redundant glycans, the search software was able to make unique N-glycopeptide assignments for 51% of assigned spectra, with the remaining assignments primarily representing isobaric topological rearrangements. The optimized workflow, coupled with GlycoPeptideSearch, is expected to make high-throughput semiautomated glycopeptide identification feasible for a wide range of users.

A simplified immunoprecipitation method for quantitatively measuring antibody responses in clinical sera samples by using mammalian-produced Renilla luciferase-antigen fusion proteins

BackgroundAssays detecting human antigen-specific antibodies are medically useful. However, the usefulness of existing simple immunoassay formats is limited by technical considerations such as sera antibodies to contaminants in insufficiently pure antigen, a problem likely exacerbated when antigen panels are screened to obtain clinically useful data.ResultsWe developed a novel and simple immunoprecipitation technology for identifying clinical sera containing antigen-specific antibodies and for generating quantitative antibody response profiles. This method is based on fusing protein antigens to an enzyme reporter, Renilla luciferase (Ruc), and expressing these fusions in mammalian cells, where mammalian-specific post-translational modifications can be added. After mixing crude extracts, sera and protein A/G beads together and incubating, during which the Ruc-antigen fusion become immobilized on the A/G beads, antigen-specific antibody is quantitated by washing the beads and adding coelenterazine substrate and measuring light production.We have characterized this technology with sera from patients having three different types of cancers. We show that 20–85% of these sera contain significant titers of antibodies against at least one of five frequently mutated and/or overexpressed tumor-associated proteins. Five of six colon cancer sera tested gave responses that were statistically significantly greater than the average plus three standard deviations of 10 control sera. The results of competition experiments, preincubating positive sera with unmodified E. coli-produced antigens, varied dramatically.ConclusionThis technology has several advantages over current quantitative immunoassays including its relative simplicity, its avoidance of problems associated with E. coli-produced antigens and its use of antigens that can carry mammalian or disease-specific post-translational modifications. This assay should be generally useful for analyzing sera for antibodies recognizing any protein or its post-translational modifications.

Site-specific Glycoforms of Haptoglobin in Liver Cirrhosis and Hepatocellular Carcinoma

Haptoglobin is a liver-secreted glycoprotein with four N-glycosylation sites. Its glycosylation was reported to change in several cancer diseases, which prompted us to examine site-specific glycoforms of haptoglobin in liver cirrhosis and hepatocellular carcinoma. To this end, we have used two-dimensional separation composed of hydrophilic interaction and nano-reverse phase chromatography coupled to QTOF mass spectrometry of the enriched glycopeptides. Our results show increased fucosylation of haptoglobin in liver disease with up to six fucoses associated with specific glycoforms of one glycopeptide. Structural analysis using exoglycosidase treatment and MALDI-MS/MS of detached permethylated glycans led to the identification of Lewis Y-type structures observed particularly in the pooled hepatocellular carcinoma sample. To confirm the increase of the Lewis Y structures observed by LC-MS, we have used immunoaffinity detection with Lewis Y-specific antibodies. The presence of multiply fucosylated Lewis Y glycoforms of haptoglobin in the disease context could have important functional implications.

Exploring Site-Specific N-Glycosylation Microheterogeneity of Haptoglobin using Glycopeptide CID Tandem Mass Spectra and Glycan Database Search

Glycosylation is a common protein modification with a significant role in many vital cellular processes and human diseases, making the characterization of protein-attached glycan structures important for understanding cell biology and disease processes. Direct analysis of protein N-glycosylation by tandem mass spectrometry of glycopeptides promises site-specific elucidation of N-glycan microheterogeneity, something that detached N-glycan and deglycosylated peptide analyses cannot provide. However, successful implementation of direct N-glycopeptide analysis by tandem mass spectrometry remains a challenge. In this work, we consider algorithmic techniques for the analysis of LC-MS/MS data acquired from glycopeptide-enriched fractions of enzymatic digests of purified proteins. We implement a computational strategy that takes advantage of the properties of CID fragmentation spectra of N-glycopeptides, matching the MS/MS spectra to peptide-glycan pairs from protein sequences and glycan structure databases. Significantly, we also propose a novel false discovery rate estimation technique to estimate and manage the number of false identifications. We use a human glycoprotein standard, haptoglobin, digested with trypsin and GluC, enriched for glycopeptides using HILIC chromatography, and analyzed by LC-MS/MS to demonstrate our algorithmic strategy and evaluate its performance. Our software, GlycoPeptideSearch (GPS), assigned glycopeptide identifications to 246 of the spectra at a false discovery rate of 5.58%, identifying 42 distinct haptoglobin peptide-glycan pairs at each of the four haptoglobin N-linked glycosylation sites. We further demonstrate the effectiveness of this approach by analyzing plasma-derived haptoglobin, identifying 136 N-linked glycopeptide spectra at a false discovery rate of 0.4%, representing 15 distinct glycopeptides on at least three of the four N-linked glycosylation sites. The software, GlycoPeptideSearch, is available for download from http://edwardslab.bmcb.georgetown.edu/GPS .