Exploration of Nitrotyrosine-Containing Proteins and Peptides by Antibody-Based Enrichment Strategies.

Nitrotyrosine, or 3-nitrotyrosine, is an oxidative post-translational modification induced by reactive nitrogen species. Although nitrotyrosine is considered a marker of oxidative stress and has been associated with inflammation, neurodegeneration, cardiovascular disease, and cancer, identification of nitrotyrosine-modified proteins remains challenging owing to its low stoichiometric levels in biological samples. To facilitate a comprehensive analysis of proteins and peptides containing nitrotyrosine, we optimized an immunoprecipitation-based enrichment workflow using a cell line model. The identification of proteins and peptides containing nitrotyrosine residues was carried out after peroxynitrite treatment of cell lysates, which generated modified nitrotyrosine residues on susceptible sites on proteins. We evaluated the efficacy of enriching nitrotyrosine-modified proteins and peptides by employing four different commercially available monoclonal antibodies directed against nitrotyrosine. LC-MS/MS analysis resulted in the identification of 1377 and 1624 nitrotyrosine-containing peptides from protein- and peptide-based enrichment experiments, respectively. Although the yield of nitrotyrosine-containing peptides was higher in experiments where peptides rather than proteins were enriched, we found a substantial proportion (37-65%) of identified nitrotyrosine-containing peptides contained nitrotyrosine at the N-terminus. However, in protein-based immunoprecipitation <9% of nitrotyrosine-containing peptides had nitrotyrosine modification at the N-terminus of the peptide. Overall, our study resulted in the identification of 2603 nitrotyrosine-containing peptides of which >2000 have not previously been reported. We synthesized 101 novel nitrotyrosine-containing peptides identified in our analysis and analyzed them by LC-MS/MS to validate our findings. We have confirmed the validity of 70% of these peptides, as they demonstrated a similarity score exceeding 0.7 when compared to peptides identified through experimental methods. Finally, we also validated the presence of nitrotyrosine modification on PKM and EF2 proteins in peroxynitrite-treated samples by immunoblot analysis. The large catalog presented in this study along with the workflow should facilitate the investigation of nitrotyrosine as an oxidative modification in a variety of settings in greater detail.

Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF.

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.

TRIT1 defect leads to a recognizable phenotype of myoclonic epilepsy, speech delay, strabismus, progressive spasticity, and normal lactate levels.

TRIT1 defect is a rare, autosomal-recessive disorder of transcription, initially described as a condition with developmental delay, myoclonic seizures, and abnormal mitochondrial function. Currently, only 13 patients have been reported. We reviewed the genetic, clinical, and metabolic aspects of the disease in all known patients, including two novel, unrelated TRIT1 cases with abnormalities in oxidative phosphorylation complexes I and IV in fibroblasts. Taken together the features of all 15 patients, TRIT1 defect could be identified as a potentially recognizable syndrome including myoclonic epilepsy, speech delay, strabismus, progressive spasticity, and variable microcephaly, with normal lactate levels. Half of the patients had oxidative phosphorylation complex measurements and had multiple complex abnormalities.

Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study.

BACKGROUND: COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications. METHODS: In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done. FINDINGS: We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile. INTERPRETATION: A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study. FUNDING: Eric and Wendy Schmidt.

Serum Proteomics on the Basis of Discovery of Predictive Biomarkers of Response to Androgen Deprivation Therapy in Advanced Prostate Cancer.

BACKGROUND: We investigated the serum proteome of hormone-sensitive prostate cancer patients to determine candidate biomarkers associated with androgen deprivation therapy (ADT) efficacy. PATIENTS AND METHODS: Serum proteomes generated using isobaric mass tags for relative and absolute quantitation were analyzed using reverse-phase liquid chromatography coupled to tandem mass spectrometry. The advanced hormone-sensitive prostate cancer cohorts studied were: (1) untreated "paired" pre-ADT and 4-month post-ADT hormone-sensitive patients (n = 15); (2) "early ADT failure" patients (n = 10) in whom ADT treatment failed within a short period of time; and (3) "late ADT failure" patients (n = 10) in whom ADT treatment failed after a prolonged response time. Differential abundance was assessed, and ingenuity pathway analysis (IPA) was used to identify interaction networks in selected candidates from these comparisons. RESULTS: Between "post-ADT" and combined "early" and "late" ADT failure groups 149 differentially detected candidates were observed, and between "early" and "late" ADT failure groups 98 candidates were observed; 47 candidates were common in both comparisons. IPA network enrichment analysis of the 47 candidates identified 3 interaction networks (P < .01) including 17-ß-estradiol, nuclear factor kappa-light-chain enhancer of activated B cells complex, and P38 mitogen-activated protein kinases as pathways with potential markers of response to ADT. CONCLUSION: A global proteomic analysis identified pathways with markers of ADT response, which will need validation in independent data sets.

Mate pair sequencing improves detection of genomic abnormalities in acute myeloid leukemia.

OBJECTIVE: Acute myeloid leukemia (AML) can be subtyped based on recurrent cytogenetic and molecular genetic abnormalities with diagnostic and prognostic significance. Although cytogenetic characterization classically involves conventional chromosome and/or fluorescence in situ hybridization (FISH) assays, limitations of these techniques include poor resolution and the inability to precisely identify breakpoints. METHOD: We evaluated whether an NGS-based methodology that detects structural abnormalities and copy number changes using mate pair sequencing (MPseq) can enhance the diagnostic yield for patients with AML. RESULTS: Using 68 known abnormal and 20 karyotypically normal AML samples, each recurrent primary AML-specific abnormality previously identified in the abnormal samples was confirmed using MPseq. Importantly, in eight cases with abnormalities that could not be resolved by conventional cytogenetic studies, MPseq was utilized to molecularly define eight recurrent AML-fusion events. In addition, MPseq uncovered two cryptic abnormalities that were missed by conventional cytogenetic studies. Thus, MPseq improved the diagnostic yield in the detection of AML-specific structural rearrangements in 10/88 (11%) of cases analyzed. CONCLUSION: Utilization of MPseq represents a precise, molecular-based technique that can be used as an alternative to conventional cytogenetic studies for newly diagnosed AML patients with the potential to revolutionize the diagnosis of hematologic malignancies.

Copy number variant analysis using genome-wide mate-pair sequencing.

Copy number variation (CNV) is a common form of structural variation detected in human genomes, occurring as both constitutional and somatic events. Cytogenetic techniques like chromosomal microarray (CMA) are widely used in analyzing CNVs. However, CMA techniques cannot resolve the full nature of these structural variations (i.e. the orientation and location of associated breakpoint junctions) and must be combined with other cytogenetic techniques, such as karyotyping or FISH, to do so. This makes the development of a next-generation sequencing (NGS) approach capable of resolving both CNVs and breakpoint junctions desirable. Mate-pair sequencing (MPseq) is a NGS technology designed to find large structural rearrangements across the entire genome. Here we present an algorithm capable of performing copy number analysis from mate-pair sequencing data. The algorithm uses a step-wise procedure involving normalization, segmentation, and classification of the sequencing data. The segmentation technique combines both read depth and discordant mate-pair reads to increase the sensitivity and resolution of CNV calls. The method is particularly suited to MPseq, which is designed to detect breakpoint junctions at high resolution. This allows for the classification step to accurately calculate copy number levels at the relatively low read depth of MPseq. Here we compare results for a series of hematological cancer samples that were tested with CMA and MPseq. We demonstrate comparable sensitivity to the state-of-the-art CMA technology, with the benefit of improved breakpoint resolution. The algorithm provides a powerful analytical tool for the analysis of MPseq results in cancer.

SVAtools for junction detection of genome-wide chromosomal rearrangements by mate-pair sequencing (MPseq).

Mate-pair sequencing (MPseq), using long-insert, paired-end genomic libraries, is a powerful next-generation sequencing-based approach for the detection of genomic structural variants. SVAtools is a set of algorithms to detect both chromosomal rearrangements and large (>10 kb) copy number variants (CNVs) in genome-wide MPseq data. SVAtools can also predict gene disruptions and gene fusions, and characterize the genomic structure of complex rearrangements. To illustrate the power of SVAtools' junction detection methods to provide comprehensive molecular karyotypes, MPseq data were compared against a set of samples previously characterized by traditional cytogenetic methods. Karyotype, FISH and chromosomal microarray (CMA), performed for 29 patients in a clinical laboratory setting, collectively revealed 285 breakpoints in 87 rearrangements. The junction detection methods of SVAtools detected 87% of these breakpoints compared to 48%, 42% and 57% for karyotype, FISH and CMA respectively. Breakpoint resolution was also reported to 1 kb or less and additional genomic rearrangement complexities not appreciable by standard cytogenetic techniques were revealed. For example, 63% of CNVs detected by CMA were shown by SVAtools' junction detection to occur secondary to a rearrangement other than a simple deletion or tandem duplication. SVAtools with MPseq provides comprehensive and accurate whole-genome junction detection with improved breakpoint resolution, compared to karyotype, FISH, and CMA combined. This approach to molecular karyotyping offers considerable diagnostic potential for the simultaneous detection of both novel and recurrent genomic rearrangements in hereditary and neoplastic disorders.

Clarifying immunoglobulin gene usage in systemic and localized immunoglobulin light-chain amyloidosis by mass spectrometry.

The goal of this study was to investigate the frequency of use of light-chain variable region (IGVL) genes among patients with systemic (ALS) and localized (ALL) amyloidosis and to assess for associations between IGVL gene usage and organ tropism. We evaluated clinic charts from 821 AL patients seen at the Mayo Clinic who had bone marrow, fat pad, and solid organ tissue samples typed by liquid chromatography tandem mass spectrometry (LC-MS). We identified 701 patients with ALS and 120 with ALL Overall, we were able to identify an IGVL gene in 87 (72%) patients with ALL and 573 (82%) patients with ALS When compared with ALL, LV6-57 was more common, whereas KV3-20 and heavy-chain codeposition were less common in ALS In this large series of ALS, characteristics particular to specific genotypes became apparent. LV6-57 patients were more likely to have renal involvement and to harbor a translocation 11;14. LV3-01 patients were less likely to have advanced cardiac disease and renal involvement. LV2-14 patients were more likely to have peripheral nerve involvement, an intact circulating immunoglobulin, and lower circulating dFLC. LV1-44 patients were more likely to have cardiac involvement. KV1-33 patients had more liver involvement and higher circulating dFLC. Finally, KV1-05 was associated with inferior overall survival but not independently of cardiac stage. IGVL gene usage appears to provide clues about disease pathophysiology and tissue tropism. LC-MS is a high-throughput and low-resource technique that can be used to identify IGVL gene from clinical tissue specimens.

Proteomic detection of immunoglobulin light chain variable region peptides from amyloidosis patient biopsies.

Immunoglobulin light chain (LC) amyloidosis (AL) is caused by deposition of clonal LCs produced by an underlying plasma cell neoplasm. The clonotypic LC sequences are unique to each patient, and they cannot be reliably detected by either immunoassays or standard proteomic workflows that target the constant regions of LCs. We addressed this issue by developing a novel sequence template-based workflow to detect LC variable (LCV) region peptides directly from AL amyloid deposits. The workflow was implemented in a CAP/CLIA compliant clinical laboratory dedicated to proteomic subtyping of amyloid deposits extracted from either formalin-fixed paraffin-embedded tissues or subcutaneous fat aspirates. We evaluated the performance of the workflow on a validation cohort of 30 AL patients, whose amyloidogenic clone was identified using a novel proteogenomics method, and 30 controls. The recall and negative predictive values of the workflow, when identifying the gene family of the AL clone, were 93 and 98%, respectively. Application of the workflow on a clinical cohort of 500 AL amyloidosis samples highlighted a bias in the LCV gene families used by the AL clones. We also detected similarity between AL clones deposited in multiple organs of systemic AL patients. In summary, AL proteomic data sets are rich in LCV region peptides of potential clinical significance that are recoverable with advanced bioinformatics.

Identification of Biomarkers for PKD1 Using Urinary Exosomes.

Autosomal dominant polycystic kidney disease (ADPKD) is a common cause of ESRD. Affected individuals inherit a defective copy of either PKD1 or PKD2, which encode polycystin-1 (PC1) or polycystin-2 (PC2), respectively. PC1 and PC2 are secreted on urinary exosome-like vesicles (ELVs) (100-nm diameter vesicles), in which PC1 is present in a cleaved form and may be complexed with PC2. Here, label-free quantitative proteomic studies of urine ELVs in an initial discovery cohort (13 individuals with PKD1 mutations and 18 normal controls) revealed that of 2008 ELV proteins, 9 (0.32%) were expressed at significantly different levels in samples from individuals with PKD1 mutations compared to controls (P<0.03). In samples from individuals with PKD1 mutations, levels of PC1 and PC2 were reduced to 54% (P<0.02) and 53% (P<0.001), respectively. Transmembrane protein 2 (TMEM2), a protein with homology to fibrocystin, was 2.1-fold higher in individuals with PKD1 mutations (P<0.03). The PC1/TMEM2 ratio correlated inversely with height-adjusted total kidney volume in the discovery cohort, and the ratio of PC1/TMEM2 or PC2/TMEM2 could be used to distinguish individuals with PKD1 mutations from controls in a confirmation cohort. In summary, results of this study suggest that a test measuring the urine exosomal PC1/TMEM2 or PC2/TMEM2 ratio may have utility in diagnosis and monitoring of polycystic kidney disease. Future studies will focus on increasing sample size and confirming these studies. The data were deposited in the ProteomeXchange (identifier PXD001075).

Clinical proteome informatics workbench detects pathogenic mutations in hereditary amyloidoses.

Shotgun proteomics of hereditary amyloid deposits generates all the information necessary to identify pathogenic mutant peptides and proteins. However, these mutant peptides are invisible to traditional database search strategies. We developed a two-pronged informatics workflow for detecting both known and novel amyloidogenic mutations from clinical proteomics data sets. We implemented the workflow in a CAP/CLIA certified clinical laboratory dedicated for proteomic subtyping of amyloid deposits extracted from formalin-fixed paraffin-embedded specimens. Performance of the workflow was characterized on a validation cohort of 49 hereditary amyloid samples, with confirmed mutations, and 85 controls. The sensitivity, specificity, positive predictive value, and negative predictive value of the known mutation detection workflow were determined to be 92%, 100%, 100%, and 96%, respectively. For novel mutation detection workflow, these performance parameters were 82%, 99%, 99%, and 90%, respectively. Validated workflow was applied to detect amyloidogenic mutations from a clinical cohort of 150 amyloid samples. The known mutation detection workflow detected rare frame shift mutations in apolipoprotein A1 and fibrinogen alpha amyloid deposits. The novel mutation detection workflow uncovered unanticipated mutations (W22G and C71Y) of the serum amyloid A4 protein present in patient amyloid deposits. In summary, clinical amyloid proteomics data sets contain mutant peptides of clinical significance that are recoverable with improved bioinformatics.

Subfractionation, characterization, and in-depth proteomic analysis of glomerular membrane vesicles in human urine.

Urinary exosome-like vesicles (ELVs) are a heterogenous mixture (diameter 40-200 nm) containing vesicles shed from all segments of the nephron including glomerular podocytes. Contamination with Tamm-Horsfall protein (THP) oligomers has hampered their isolation and proteomic analysis. Here we improved ELV isolation protocols employing density centrifugation to remove THP and albumin, and isolated a glomerular membranous vesicle (GMV)-enriched subfraction from 7 individuals identifying 1830 proteins and in 3 patients with glomerular disease identifying 5657 unique proteins. The GMV fraction was composed of podocin/podocalyxin-positive irregularly shaped membranous vesicles and podocin/podocalyxin-negative classical exosomes. Ingenuity pathway analysis identified integrin, actin cytoskeleton, and Rho GDI signaling in the top three canonical represented signaling pathways and 19 other proteins associated with inherited glomerular diseases. The GMVs are of podocyte origin and the density gradient technique allowed isolation in a reproducible manner. We show many nephrotic syndrome proteins, proteases, and complement proteins involved in glomerular disease are in GMVs and some were only shed in the disease state (nephrin, TRPC6, INF2 and phospholipase A2 receptor). We calculated sample sizes required to identify new glomerular disease biomarkers, expand the ELV proteome, and provide a reference proteome in a database that may prove useful in the search for biomarkers of glomerular disease.

Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Shotgun proteomics via mass spectrometry (MS) is a powerful technology for biomarker discovery that has the potential to lead to noninvasive disease screening mechanisms. Successful application of MS-based proteomics technologies for biomarker discovery requires accurate expectations of bias, reproducibility, variance, and the true detectable differences in platforms chosen for analyses. Characterization of the variability inherent in MS assays is vital and should affect interpretation of measurements of observed differences in biological samples. Here we describe observed biases, variance structure, and the ability to detect known differences in spike-in data sets for which true relative abundance among defined samples were known and were subsequently measured with the iTRAQ technology on two MS platforms. Global biases were observed within these data sets. Measured variability was a function of mean abundance. Fold changes were biased toward the null and variance of a fold change was a function of protein mass and abundance. The information presented herein will be valuable for experimental design and analysis of the resulting data.

Bi-Linear Regression for O Quantification: Modeling across the Elution Profile.

MOTIVATION: Interpreting and quantifying labeled mass-spectrometry data is complex and requires automated algorithms, particularly for large scale proteomic profiling. Here, we propose the use of bi-linear regression to quantify relative abundance across the elution profile in a unified model. The bi-linear regression model takes advantage of the fact that while peptides differ in overall abundance across the elution profile multiplicatively, the relative abundance between the mixed samples remains constant across the elution profile. We describe how to apply bi-linear regression models to (18)O stable-isotope labeled data, which allows for the direct comparison of two samples simultaneously. Interpretation of model parameters is also discussed. The incorporation rate of the labeling isotope is estimated as part of the modeling process and can be used as a measure of data quality. Application is demonstrated in a controlled experiment as well as in a complex mixture. RESULTS: Bi-linear regression models allow for more precise and accurate estimates of abundance, in comparison to methods that treat each spectrum independently, by taking into account the abundance of the molecule throughout the entire elution profile, with precision increased by one-to-two orders of magnitude.