Surendra Dasari

IDPicker 2.0: Improved protein assembly with high discrimination peptide identification filtering.

Tandem mass spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. A number of database searching algorithms have been developed to assign peptide sequences to tandem mass spectra. Assembling the peptide identifications to proteins, however, is a challenging issue because many peptides are shared among multiple proteins. IDPicker is an open-source protein assembly tool that derives a minimum protein list from peptide identifications filtered to a specified False Discovery Rate. Here, we update IDPicker to increase confident peptide identifications by combining multiple scores produced by database search tools. By segregating peptide identifications for thresholding using both the precursor charge state and the number of tryptic termini, IDPicker retrieves more peptides for protein assembly. The new version is more robust against false positive proteins, especially in searches using multispecies databases, by requiring additional novel peptides in the parsimony process. IDPicker has been designed for incorporation in many identification workflows by the addition of a graphical user interface and the ability to read identifications from the pepXML format. These advances position IDPicker for high peptide discrimination and reliable protein assembly in large-scale proteomics studies. The source code and binaries for the latest version of IDPicker are available from http://fenchurch.mc.vanderbilt.edu/ .

Proteomic Identification of Salivary Biomarkers of Type-2 Diabetes

The identification of biomarkers to noninvasively detect prediabetes/diabetes will facilitate interventions designed to prevent or delay progression to frank diabetes and its attendant complications. The purpose of this study was to characterize the human salivary proteome in type-2 diabetes to identify potential biomarkers of diabetes. Whole saliva from control and type-2 diabetic individuals was characterized by multidimensional liquid chromatography/tandem mass spectrometry (2D-LC-MS/MS). Label-free quantification was used to identify differentially abundant protein biomarkers. Selected potential biomarkers were then independently validated in saliva from control, diabetic, and prediabetic subjects by Western immunoblotting and ELISA. Characterization of the salivary proteome identified a total of 487 unique proteins. Approximately 33% of these have not been previously reported in human saliva. Of these, 65 demonstrated a greater than 2-fold difference in abundance between control and type-2 diabetes samples. A majority of the differentially abundant proteins belong to pathways regulating metabolism and immune response. Independent validation of a subset of potential biomarkers utilizing immunodetection confirmed their differential expression in type-2 diabetes, and analysis of prediabetic samples demonstrated a trend of relative increase in their abundance with progression from the prediabetic to the diabetic state. This comprehensive proteomic analysis of the human salivary proteome in type-2 diabetes provides the first global view of potential mechanisms perturbed in diabetic saliva and their utility in detection and monitoring of diabetes. Further characterization of these markers in a larger cohort of subjects may provide the basis for new, noninvasive tests for diabetes screening, detection, and monitoring.

TagRecon: high-throughput mutation identification through sequence tagging.

Shotgun proteomics produces collections of tandem mass spectra that contain all the data needed to identify mutated peptides from clinical samples. Identifying these sequence variations, however, has not been feasible with conventional database search strategies, which require exact matches between observed and expected sequences. Searching for mutations as mass shifts on specified residues through database search can incur significant performance penalties and generate substantial false positive rates. Here we describe TagRecon, an algorithm that leverages inferred sequence tags to identify unanticipated mutations in clinical proteomic data sets. TagRecon identifies unmodified peptides as sensitively as the related MyriMatch database search engine. In both LTQ and Orbitrap data sets, TagRecon outperformed state of the art software in recognizing sequence mismatches from data sets with known variants. We developed guidelines for filtering putative mutations from clinical samples, and we applied them in an analysis of cancer cell lines and an examination of colon tissue. Mutations were found in up to 6% of identified peptides, and only a small fraction corresponded to dbSNP entries. The RKO cell line, which is DNA mismatch repair deficient, yielded more mutant peptides than the mismatch repair proficient SW480 line. Analysis of colon cancer tumor and adjacent tissue revealed hydroxyproline modifications associated with extracellular matrix degradation. These results demonstrate the value of using sequence tagging algorithms to fully interrogate clinical proteomic data sets.

Clinical diagnosis and typing of systemic amyloidosis in subcutaneous fat aspirates by mass spectrometry-based proteomics

Examination of abdominal subcutaneous fat aspirates is a practical, sensitive and specific method for the diagnosis of systemic amyloidosis. Here we describe the development and implementation of a clinical assay using mass spectrometry-based proteomics to type amyloidosis in subcutaneous fat aspirates. First, we validated the assay comparing amyloid-positive (n=43) and -negative (n=26) subcutaneous fat aspirates. The assay classified amyloidosis with 88% sensitivity and 96% specificity. We then implemented the assay as a clinical test, and analyzed 366 amyloid-positive subcutaneous fat aspirates in a 4-year period as part of routine clinical care. The assay had a sensitivity of 90%, and diverse amyloid types, including immunoglobulin light chain (74%), transthyretin (13%), serum amyloid A (%1), gelsolin (1%), and lysozyme (1%), were identified. Using bioinformatics, we identified a universal amyloid proteome signature, which has high sensitivity and specificity for amyloidosis similar to that of Congo red staining. We curated proteome databases which included variant proteins associated with systemic amyloidosis, and identified clonotypic immunoglobulin variable gene usage in immunoglobulin light chain amyloidosis, and the variant peptides in hereditary transthyretin amyloidosis. In conclusion, mass spectrometry-based proteomic analysis of subcutaneous fat aspirates offers a powerful tool for the diagnosis and typing of systemic amyloidosis. The assay reveals the underlying pathogenesis by identifying variable gene usage in immunoglobulin light chains and the variant peptides in hereditary amyloidosis.

Using Mass Spectrometry to Monitor Monoclonal Immunoglobulins in Patients with a Monoclonal Gammopathy

A monoclonal gammopathy is defined by the detection a monoclonal immunoglobulin (M-protein). In clinical practice, the M-protein is detected by protein gel electrophoresis (PEL) and immunofixation electrophoresis (IFE). We theorized that molecular mass could be used instead of electrophoretic patterns to identify and quantify the M-protein because each light and heavy chain has a unique amino acid sequence and thus a unique molecular mass whose increased concentration could be distinguished from the normal polyclonal background. In addition, we surmised that top-down MS could be used to isotype the M-protein because each immunoglobulin has a constant region with an amino acid sequence unique to each isotype. Our method first enriches serum for immunoglobulins followed by reduction using DTT to separate light chains from heavy chains and then by microflow LC-ESI-Q-TOF MS. The multiply charged light and heavy chain ions are converted to their molecular masses, and reconstructed peak area calculations for light chains are used for quantification. Using this method, we demonstrate how the light chain portion of an M-protein can be monitored by molecular mass, and we also show that in sequential samples from a patient with multiple myeloma the light chain portion of the M-protein was detected in all samples, even those negative by PEL, IFE, and quantitative FLC. We also present top-down MS isotyping of M-protein light chains using a unique isotype-specific fragmentation pattern allowing for quantification and isotype identification in the same run. Our results show that microLC-ESI-Q-TOF MS provides superior sensitivity and specificity compared to conventional methods and shows promise as a viable method of detecting and isotyping an M-protein.

Comprehensive maternal serum proteomic profiles of preclinical and clinical preeclampsia

We systematically characterized maternal serum proteome in women with clinical preeclampsia (PE) and asymptomatic women in early pregnancy that subsequently developed PE. Clinical PE cohort comprised 30 patients with mild PE, 30 with severe PE, and 58 normotensive women. Preclinical PE cohort included 149 women whose serum samples were collected at 8-14 gestational weeks and in whom 30 women later developed mild and 40 severe PE. Serum proteome was analyzed and enzyme-linked immunosorbent assays were used for protein quantification. In Clinical PE, fibronectin, pappalysin-2, choriogonadotropin-beta, apolipoprotein C-III, cystatin-C, vascular endothelial growth factor receptor-1, and endoglin were more abundant compared to normotensive women. In preclinical PE, differently expressed proteins included placental, vascular, transport, matrix, and acute phase proteins. Angiogenic and antiangiogenic proteins were not significant. We conclude that placental and antiangiogenic proteins are abundant in clinical PE. In preclinical PE, proteomic profile is distinct and different from that in clinical PE.

QuaMeter: Multivendor Performance Metrics for LC–MS/MS Proteomics Instrumentation

LC-MS/MS-based proteomics studies rely on stable analytical system performance that can be evaluated by objective criteria. The National Institute of Standards and Technology (NIST) introduced the MSQC software to compute diverse metrics from experimental LC-MS/MS data, enabling quality analysis and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufactured by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment.

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.

Noninvasive diagnosis of intraamniotic infection: proteomic biomarkers in vaginal fluid

OBJECTIVE We analyzed the vaginal fluid proteome to identify biomarkers of intraamniotic infection among women in preterm labor. STUDY DESIGN Proteome analysis was performed on vaginal fluid specimens from women with preterm labor, using multidimensional liquid chromatography, tandem mass spectrometry, and label-free quantification. Enzyme immunoassays were used to quantify candidate proteins. Classification accuracy for intraamniotic infection (positive amniotic fluid bacterial culture and/or interleukin-6 >2 ng/mL) was evaluated using receiver-operator characteristic curves obtained by logistic regression. RESULTS Of 170 subjects, 30 (18%) had intraamniotic infection. Vaginal fluid proteome analysis revealed 338 unique proteins. Label-free quantification identified 15 proteins differentially expressed in intraamniotic infection, including acute-phase reactants, immune modulators, high-abundance amniotic fluid proteins and extracellular matrix-signaling factors; these findings were confirmed by enzyme immunoassay. A multi-analyte algorithm showed accurate classification of intraamniotic infection. CONCLUSION Vaginal fluid proteome analyses identified proteins capable of discriminating between patients with and without intraamniotic infection.

Leukocyte cell-derived chemotaxin 2 (LECT2)–associated amyloidosis is a frequent cause of hepatic amyloidosis in the United States

Using laser microdissection and mass spectrometry (MS)-based proteomics, we subtyped amyloid deposits from 130 cases of hepatic amyloidosis. Although we confirmed that immunoglobulin light chain amyloidosis was the most frequent cause of hepatic amyloidosis, leukocyte cell-derived chemotaxin 2 (LECT2) amyloidosis (ALect2) accounted for 25% of cases. This novel finding was associated with Hispanic ancestry, incidental discovery of amyloid in liver specimens sampled for other unrelated conditions, and a characteristic pattern of hepatic amyloid deposition. Although ALect2 patients had a common LECT2 polymorphism, pathogenic mutations were not discovered, suggesting that constitutive or compensatory LECT2 overexpression led to ALect2 deposition. These findings indicate that ALect2 is a common cause of hepatic amyloidosis in the population of the United States, and subtyping hepatic amyloid deposits by an accurate analytic method such as MS is required for optimal clinical management of hepatic amyloidosis patients and to avoid incorrect and unnecessarily toxic therapies.