Mass Spectrometry-based Shotgun Proteomics Research Articles

Exploring the cellulolytic activity of environmental mycobacteria

Although studies on non-tuberculous mycobacteria have increased in recent years because they cause a considerable proportion of infections, their cellulolytic system is still poorly studied. This study presents a characterization of the cellulolytic activities of environmental mycobacterial isolates derived from soil and water samples from the central region of Argentina, aimed to evaluate the conservation of the mechanism for the degradation of cellulose in this group of bacteria. The molecular and genomic identification revealed identity with Mycolicibacterium septicum. The endoglucanase and total cellulase activities were assessed both qualitatively and quantitatively and the optimal enzymatic conditions were characterized. A specific protein of around 56 kDa with cellulolytic activity was detected in a zymogram. Protein sequences possibly arising from a cellulase were identified by mass spectrometry-based shotgun proteomics. Results showed that M. septicum encodes for cellulose- and hemicellulose-related degrading enzymes, including at least an active β-1,4 endoglucanase enzyme that could be useful to improve its survival in the environment. Given the important health issues related to mycobacteria, the results of the present study may contribute to the knowledge of their cellulolytic system, which could be important for their ability to survive in many different types of environments.

Proteomic patterns in glomerular research, a laser capture microdissection and liquid chromatography-tandem mass spectrometry approach

Abstract Introduction: Molecular techniques have the potential to shed light on glomerular diseases that conventional renal pathology may be unable to reveal. The aim of this study was to investigate whether proteomic patterns of glomeruli obtained from kidney biopsies can differentiate between minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS) and control groups (CTR). Methods: 18 formalin-fixed, paraffin-embedded (FFPE) renal biopsies comprising three groups of samples (CTR=3, MCD=6, FSGS=9) were subjected to label-free quantitative mass spectrometry. Glomeruli were excised from FFPE renal biopsies by laser capture microdissection (LCM) and, to increase both yield and protein identifications, single-pot solid-phase-enhanced sample preparation (SP3) digest method was applied. The samples were analyzed by mass spectrometry based shotgun proteomics. Results: The proteome profiling resulted in the identification of a total of 723 proteins. Multivariate analysis provided several proteins important in the separation of the three groups. Pattern Hunter analysis revealed moderate and high correlation of proteins against CTR-FSGS-MCD or CTR-MCD-FSGS patterns. The most significant pathways involved were associated with nephrin family and cytoskeleton interactions, as well as laminin/extracellular matrix related proteins. Univariate analysis revealed 58 significant different proteins among the three groups. Signaling pathways of these proteins were also associated with nephrin family interactions and cytoskeleton organization. Conclusions: This study demonstrates that mass spectrometry-based shotgun proteomic analysis of LCM glomeruli yields reproducible and quantitative data capable of discriminating between different disease conditions. Differentially expressed proteins provide insights into pathogenesis of glomerular disease.

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.

Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, correct taxonomic inference is crucial when identifying different viral strains with high sequence homology-considering, e.g., the different epidemiological characteristics of the various strains of SARS-CoV-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. PepGM is written in Python and embedded into a Snakemake workflow. It is available on https://github.com/BAMeScience/PepGM.

A Proteomics Data Mining Strategy for the Identification of Quinoa Grain Proteins with Potential Immunonutritional Bioactivities.

Quinoa proteins are attracting global interest for their wide amino acid profile and as a promising source for the development of biomedical treatments, including those against immune-mediated diseases. However, information about the bioactivity of quinoa proteins is scarce. In this study, a quinoa grain proteome map obtained by label-free mass spectrometry-based shotgun proteomics was investigated for the identification of quinoa grain proteins with potential immunonutritional bioactivities, including those related to cancer. After carefully examining the sequence similarities of the 1211 identified quinoa grain proteins against already described bioactive proteins from other plant organisms, 71, 48, and 3 of them were classified as antimicrobial peptides (AMPs), oxidative stress induced peptides (OSIPs), and serine-type protease inhibitors (STPIs), respectively, suggesting their potential as immunomodulatory, anti-inflammatory, and anticancer agents. In addition, data interpretation using Venn diagrams, heat maps, and scatterplots revealed proteome similarities and differences with respect to the AMPs, OSIPs, and STPIs, and the most relevant bioactive proteins in the predominant commercial quinoa grains (i.e., black, red, white (from Peru), and royal (white from Bolivia)). The presented proteomics data mining strategy allows easy screening for potentially relevant quinoa grain proteins and commercial classes for immunonutrition, as a basis for future bioactivity testing.

Longitudinal Proteomic Analysis of Plasma across Healthy Pregnancies Reveals Indicators of Gestational Age

Longitudinal changes in the blood proteome during gestation relate to fetal development and maternal homeostasis. Charting the maternal blood proteome in normal pregnancies is critical for establishing a baseline reference when assessing complications and disease. Using mass spectrometry-based shotgun proteomics, we surveyed the maternal plasma proteome across uncomplicated pregnancies. Results indicate a significant rise in proteins that govern placentation and are vital to the development and health of the fetus. Importantly, we uncovered proteome signatures that strongly correlated with gestational age. Fold increases and correlations between the plasma concentrations of ADAM12 (ρ = 0.973), PSG1 (ρ = 0.936), and/or CSH1/2 (ρ = 0.928) with gestational age were validated with ELISA. Proteomic and validation analyses demonstrate that the maternal plasma concentration of ADAM12, either independently or in combination with either PSG1 or CSH1/2, correlates with gestational age within ±8 days throughout pregnancy. These findings suggest that the gestational age in healthy pregnancies may be determined by referencing the concentration of select plasma proteins.

Cannabinoids modulate proliferation, differentiation, and migration signaling pathways in oligodendrocytes.

Cannabinoid signaling, mainly via CB1 and CB2 receptors, plays an essential role in oligodendrocyte health and functions. However, the specific molecular signals associated with the activation or blockade of CB1 and CB2 receptors in this glial cell have yet to be elucidated. Mass spectrometry-based shotgunproteomics and in silico biology tools were used to determine which signaling pathways and molecular mechanisms are triggered in a human oligodendrocytic cell line (MO3.13) by several pharmacological stimuli: the phytocannabinoid cannabidiol (CBD); CB1 and CB2 agonists ACEA, HU308, and WIN55, 212-2; CB1 and CB2 antagonists AM251 and AM630; and endocannabinoids anandamide (AEA) and 2-arachidonoylglycerol (2-AG). The modulation of cannabinoid signaling in MO3.13 was found to affect pathways linked to cell proliferation, migration, and differentiation of oligodendrocyte progenitor cells. Additionally, we found that carbohydrate and lipid metabolism, as well as mitochondrial function, were modulated by these compounds. Comparing the proteome changes and upstream regulators among treatments, the highest overlap was between the CB1 and CB2 antagonists, followed by overlaps between AEA and 2-AG. Our study opens new windows of opportunities, suggesting that cannabinoid signaling in oligodendrocytes might be relevant in the context of demyelinating and neurodegenerative diseases.Proteomics data are available at ProteomeXchange (PXD031923).

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples.

Recent advances in mass spectrometry have resulted in deep proteomicanalysis along with the generation ofrobust andreproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions. Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot.

Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, which can take up significant person-hours of bench labor and present a source of batch-to-batch variability. Lab automation with pipetting robots can reduce manual work, maximize throughput, and increase research reproducibility. Still, the steep starting prices of standard automation stations make them unaffordable for many academic laboratories. This article describes a proteomics sample preparation workflow using an affordable, open-source automation system (The Opentrons OT-2), including instructions for setting up semi-automated protein reduction, alkylation, digestion, and clean-up steps; as well as accompanying open-source Python scripts to program the OT-2 system through its application programming interface.

Characterization and differentiation of quinoa seed proteomes by label-free mass spectrometry-based shotgun proteomics

Quinoa seed proteins are of prime importance in human nutrition and in plant breeding for cultivar identification and improvement. In this study, proteins from seeds of black, red, white quinoa from Peru and white quinoa from Bolivia (also known as royal) were extracted, digested and analyzed by nano-liquid chromatography coupled to Orbitrap tandem mass spectrometry (LC-MS/MS). The raw mass spectra data were processed for identification and label-free quantification (LFQ) using MaxQuant/Andromeda against a specific quinoa database from The National Center for Biotechnology Information (NCBI). In total, 1,211 quinoa proteins (85 were uncharacterized) were identified. Inspection and visualization using Venn diagrams, heat maps and Gene Ontology (GO) graphs revealed proteome similarities and differences between the four varieties. The presented data provides the most comprehensive experimental quinoa seed proteome map existing to date in the literature, as a starting point for more specific characterization and nutritional studies of quinoa and quinoa-containing foodstuff.

Comparative database search engine analysis on massive tandem mass spectra of pork-based food products for halal proteomics

Mass spectrometry-based proteomics relies on dedicated software for peptide and protein identification. These software include open-source or commercial-based search engines; wherein, they employ different algorithms to establish their scoring and identified proteins. Although previous comparative studies have differentiated the proteomics results from different software, there are still yet studies specifically been conducted to compare and evaluate the search engine in the field of halal analysis. This is important because the halal analysis is often using commercial meat samples that have been subjected to various processing, further complicating its analysis. Thus, this study aimed to assess three open-source search engines (Comet, X! Tandem, and ProteinProspector) and a commercial-based search engine (ProteinPilot™) against 135 raw tandem mass spectrometry data files from 15 types of pork-based food products for halal analysis. Each database search engine contained high false-discovery rate (FDR); however, a post-searching algorithm called PeptideProphet managed to reduce the FDR, except for ProteinProspector and ProteinPilot™. From this study, the combined database search engine (executed by iProphet) reveals a thorough protein list for pork-based food products; wherein the most abundant proteins are myofibrillar proteins. Thus, this proteomics study will aid the identification of potential peptide and protein biomarkers for future precision halal analysis. SignificanceA critical challenge of halal proteomics is the availability of a database to confirm the inferential peptides as well as proteins. Currently, the established database such as UniProtKB is related to animal proteome; however, the halal proteomics is related to the highly processed meat-based food products. This study highlights the use of different database search engines (Comet, X! Tandem, ProteinProspector, and ProteinPilot™) and their respective algorithms to analyse 135 raw tandem mass spectrometry data files from 15 types of pork-based food products. This is the first attempt that has compared different database search engines in the context of halal proteomics to ensure the effectiveness of controlling the FDR. Previous studies were just focused on the advantages of a certain algorithm over another. Moreover, other previous studies also have mainly reported the use of mass spectrometry-based shotgun proteomics for meat authentication (the most similar field to halal analysis), but none of the studies were reported on halal aspects that used samples originated from highly processed food products. Hence, a systematic comparative study is duly needed for a more comprehensive and thorough proteomics analysis for such samples. In this study, our combinatorial approach for halal proteomics results from the different search engines used (Comet, X! Tandem, and ProteinProspector) has successfully generated a comprehensive spectral library for the pork-based meat products. This combined spectral library is freely available at https://data.mendeley.com/datasets/6dmm8659rm/3. Thus far, this is the first and new attempt at establishing a spectral library for halal proteomics. We also believe this study is a pioneer for halal proteomics that aimed at non-conventional and non-model organism proteomics, protein analytics, protein bioinformatics, and potential biomarker discovery.

Proteomic Analysis of Caspofungin-Induced Responses in Planktonic Cells and Biofilms of Candida albicans.

Candida albicans biofilms display markedly increased antifungal resistance, and the underlying mechanisms remain unclear. This study investigated the signature profiles of C. albicans planktonic cells and biofilms in response to caspofungin (CAS) by mass spectrometry-based shotgun proteomics. We found that C. albicans biofilms were twofold more resistant to CAS with reference to planktonic cells. Notably, 9.6% of C. albicans biofilm cells survived the lethal treatment of CAS (128 μg/ml), confirmed by LIVE/DEAD staining, confocal laser scanning microscopy (CLSM) and scanning electron microscopy analyses. The responses of C. albicans planktonic cells and biofilms to CAS treatment at respective minimum inhibitory concentrations (MICs) were assessed by high-throughput proteomics and bioinformatics approaches. There were 148 and 224 proteins with >twofold difference identified from the planktonic cells and biofilms, respectively. CAS treatment downregulated several cell wall- and oxidative stress-related proteins. Whereas, CAS-induced action was compensated by markedly increased expression of many other proteins involved in cell wall integrity and stress response (e.g., heat shock proteins). Moreover, considerable expression changes were identified in metabolism-associated proteins like glycolysis, tricarboxylic acid (TCA) cycle and ATP biosynthesis. Importantly, various key proteins for cell wall integrity, stress response and metabolic regulation (e.g., PIL1, LSP1, HSP90, ICL1, and MLS1) were exclusively enriched and implicated in C. albicans biofilms. This study demonstrates that C. albicans biofilms undergo highly complicated yet complex regulation of multiple cellular pathways in response to CAS. Signature proteins essential for modulating cell wall integrity, stress response and metabolic activities may account for the antifungal resistance of C. albicans biofilms.

Systemic amyloidosis from A (AA) to T (ATTR): a review.

Systemic amyloidosis is a rare protein misfolding and deposition disorder leading to progressive organ failure. There are over 15 types of systemic amyloidosis, each caused by a different precursor protein which promotes amyloid formation and tissue deposition. Amyloidosis can be acquired or hereditary and can affect various organs, including the heart, kidneys, liver, nerves, gastrointestinal tract, lungs, muscles, skin and soft tissues. Symptoms are usually insidious and nonspecific resulting in diagnostic delay. The field of amyloidosis has seen significant improvements over the past decade in diagnostic accuracy, prognosis prediction and management. The advent of mass spectrometry-based shotgun proteomics has revolutionized amyloid typing and has led to the discovery of new amyloid types. Accurate typing of the precursor protein is of paramount importance as the type dictates a specific management approach. In this article, we review each type of systemic amyloidosis to provide the practitioner with practical tools to improve diagnosis and management of these rare disorders.

Amyloid Typing by Mass Spectrometry in Clinical Practice: a Comprehensive Review of 16,175 Samples

ObjectiveTo map the occurrence of amyloid types in a large clinical cohort using mass spectrometry-based shotgun proteomics, an unbiased method that unambiguously identifies all amyloid types in a single assay. MethodsA mass spectrometry-based shotgun proteomics assay was implemented in a central reference laboratory. We documented our experience of typing 16,175 amyloidosis specimens over an 11-year period from January 1, 2008, to December 31, 2018. ResultsWe identified 21 established amyloid types, including AL (n=9542; 59.0%), ATTR (n=4600; 28.4%), ALECT2 (n=511; 3.2%), AA (n=463; 2.9%), AH (n=367; 2.3%), AIns (n=182; 1.2%), KRT5-14 (n=94; <1%), AFib (n=71; <1%), AApoAIV (n=57; <1%), AApoA1 (n=56; <1%), AANF (n=47; <1%), Aβ2M (n=38; <1%), ASem1 (n=34; <1%), AGel (n=29; <1%), TGFB1 (n=29; <1%), ALys (n=15; <1%), AIAPP (n=13; <1%), AApoCII (n=11; <1%), APro (n=8; <1%), AEnf (n=6; <1%), and ACal (n=2; <1%). We developed the first comprehensive organ-by-type map showing the relative frequency of 21 amyloid types in 31 different organs, and the first type-by-organ map showing organ tropism of 18 rare types. Using a modified bioinformatics pipeline, we detected amino acid substitutions in cases of hereditary amyloidosis with 100% specificity. ConclusionAmyloid typing by proteomics, which effectively recognizes all amyloid types in a single assay, optimally supports the diagnosis and treatment of amyloidosis patients in routine clinical practice.

Proteomic Approaches to Identify Cold-Regulated Plasma Membrane Proteins.

Plasma membrane is the primary determinant of freezing tolerance in plants because of its central role in freeze-thaw cycle. Changes in plasma membrane protein composition have been one of the major research areas in plant cold acclimation. To obtain comprehensive profiles of the plasma membrane proteomes and their changes during the cold acclimation process, a plasma membrane purification method using a dextran-polyethylene glycol two polymer system and a mass spectrometry-based shotgun proteomics method using nano-LC-MS/MS for the plasma membrane proteins are described. The proteomic results obtained are further applied to label-free protein semiquantification.

Analysis of venom sac constituents from the solitary, aculeate wasp Cerceris rybyensis

Solitary aculeate wasps are abundant and diverse hymenopteran insects that disable prey using venom. The venom may possess neuromodulation, immunomodulatory, metabolic-modulatory and antimicrobial functions. Venom analysis of transcriptomes and proteomes has been previously performed in social and parasitoid wasp species. We develop methodologies including mass spectrometry-based shotgun proteomics to analyse the protein constituents from venom sacs of the solitary aculeate wasp Cerceris rybyensis. The venom sac constituents of C. rybyensis are discussed with respect to other wasp species.

Development of LysargiNase, a mirror trypsin and its application in proteomics

Proteomics is a fast-growing discipline that aims at systematic identification, quantification of proteins and their post-translational modifications in cells. Mass spectrometry-based shotgun proteomics technology is currently one of the mainstream methods for proteomics research. With this method, proteins need to be digested to peptides by site-specific proteases before they can be detected with mass spectrometry. Therefore, site-specific proteases played key roles in this process and so far, a variety of specific proteases have been developed and used in proteomics study. Particularly, the identification, characterization and development of proteases that cleave at the N-termini of corresponding amino acid residues, which are just mirrors to those of typical C-termini proteases, provide novel tools for proteomics analysis. In this review, we summarized the proprieties of LysargiNase, a most recently identified mirror trypsin, and its applications in proteomics research to promote its more widespread usage.

Circulating Markers of Inflammation Persist in Children and Adults With Giant Aneurysms After Kawasaki Disease.

The sequelae of Kawasaki disease (KD) vary widely with the greatest risk for future cardiovascular events among those who develop giant coronary artery aneurysms (CAA). We sought to define the molecular signature associated with different outcomes in pediatric and adult KD patients. Molecular profiling was conducted using mass spectrometry-based shotgun proteomics, transcriptomics, and glycomics methods on 8 pediatric KD patients at the acute, subacute, and convalescent time points. Shotgun proteomics was performed on 9 KD adults with giant CAA and matched healthy controls. Plasma calprotectin was measured by ELISA in 28 pediatric KD patients 1 year post-KD, 70 adult KD patients, and 86 healthy adult volunteers. A characteristic molecular profile was seen in pediatric patients during the acute disease, which resolved at the subacute and convalescent periods in patients with no coronary artery sequelae but persisted in 2 patients who developed giant CAA. We, therefore, investigated persistence of inflammation in KD adults with giant CAA by shotgun proteomics that revealed a signature of active inflammation, immune regulation, and cell trafficking. Correlating results obtained using shotgun proteomics in the pediatric and adult KD cohorts identified elevated calprotectin levels in the plasma of patients with CAA. Investigation of expanded pediatric and adult KD cohorts revealed elevated levels of calprotectin in pediatric patients with giant CAA 1 year post-KD and in adult KD patients who developed giant CAA in childhood. Complex patterns of biomarkers of inflammation and cell trafficking can persist long after the acute phase of KD in patients with giant CAA. Elevated levels of plasma calprotectin months to decades after acute KD and infiltration of cells expressing S100A8 and A9 in vascular tissues suggest ongoing, subclinical inflammation. Calprotectin may serve as a biomarker to inform the management of KD patients following the acute illness.

Abstract P5-08-01: DPYSL3 modulates mitosis, migration and epithelial to mesenchymal transition in claudin-low breast cancer

Abstract Proteogenomics is the field of integrating data from mass spectrometry-based shotgun proteomics, and phosphoproteomics into next-generation RNA and DNA sequencing data analysis pipelines that promises new insights into cancer biology and therapeutic targeting. As well as analyses of clinical samples for disease phenotype association analysis, the application of proteogenomics to model systems also has considerable potential. A Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomic analysis prioritized dihydropyrimidinase-like-3 (DPYSL3) as a multi-level (RNA/Protein/Phosphoprotein) expression outlier specific to the Claudin-Low (CLOW) subset of triple negative breast cancers. A Pubmed informatics tool indicated a paucity of data in the context of breast cancer which further prioritized DPYSL3 for study. DPYSL3 was identified as a protein that is regulated during neuronal differentiation in the cerebral cortex and in neuronal cell lines and plays a role in regulating neurite outgrowth somehow through an association with vesicles in the growth cone. In addition, DPYSL3 expression has been observed in several malignant tumors, including prostate cancer, pancreatic cancer, gastric cancer and neuroblastoma. DPYSL3 is reported to play a role in cell migration and metastasis suppression in prostate cancer. However, in pancreatic cancer, DPYSL3 is positively associated with liver metastasis and poor outcome. DPYSL3 knock-down in DPYSL3 (+) CLOW cell lines demonstrated reduced proliferation, yet enhanced motility and increased expression of Epithelial to Mesenchymal Transition (EMT) markers suggesting that DPYSL3 is a multi-functional signaling modulator. Slower proliferation in DPYSL3 (-) CLOW cells was associated with accumulation of multi-nucleated cells indicating a mitotic defect that was associated with a collapse of the vimentin (VIM) microfilament networkinduced by VIM hyperphosphorylation. On the other hand, DPYSL3 suppressed the expression of EMT regulators TWIST and SNAIL and opposed p21 activated kinase 2 (PAK2) dependent migration, but these EMT regulators in turn induced DPYSL3 expression, suggesting DPYSL3 participates in negative feedback in EMT. Cell migration in DPYSL3 (-) cells correlated with increased phosphorylation of PAK2 on Ser20 and was sensitive to PAK2 siRNA and pharmacological PAK inhibition.Immunoprecipitation and mass spectrometry-based proteomics or western blotting strongly suggests that PAKs interact such that DPYSL3 may function as a direct negative regulator of PAK family kinases. Thus, a PAK inhibitor could potentially mitigate increase migration as an adverse effect of DPYSL3 suppression. In conclusion, DPYSL3 is a remarkable multifunctional signaling scaffold that should be examined further to provide insights into the stem cell-like state of claudin-low breast cancers, particularly in terms of their cell cycle dependencies, migratory activity and capacity for EMT. Citation Format: Matsunuma R, Chan DW, Kim B-J, Singh P, Han A, Saltzman A, Cheng C, Lei JT, Sahin E, Leng M, Fan C, Perou CM, Malovannaya A, Ellis MJ. DPYSL3 modulates mitosis, migration and epithelial to mesenchymal transition in claudin-low breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-08-01.

Quality control of single amino acid variations detected by tandem mass spectrometry

Study of single amino acid variations (SAVs) of proteins, resulting from single nucleotide polymorphisms, is of great importance for understanding the relationships between genotype and phenotype. In mass spectrometry based shotgun proteomics, identification of peptides with SAVs often suffers from high error rates on the variant sites detected. These site errors are due to multiple reasons and can be confirmed by manual inspection or genomic sequencing. Here, we present a software tool, named SAVControl, for site-level quality control of variant peptide identifications. It mainly includes strict false discovery rate control of variant peptide identifications and variant site verification by unrestrictive mass shift relocalization. SAVControl was validated on three colorectal adenocarcinoma cell line datasets with genomic sequencing evidences and tested on a colorectal cancer dataset from The Cancer Genome Atlas. The results show that SAVControl can effectively remove false detections of SAVs. SignificanceProtein sequence variations caused by single nucleotide polymorphisms (SNPs) are single amino acid variations (SAVs). The investigation of SAVs may provide a chance for understanding the relationships between genotype and phenotype. Mass spectrometry (MS) based proteomics provides a large-scale way to detect SAVs. However, using the current analysis strategy to detect SAVs may lead to high rate of false positives. The SAVControl we present here is a computational workflow and software tool for site-level quality control of SAVs detected by MS. It accesses the confidence of detected variant sites by relocating the mass shift responsible for an SAV to search for alternative interpretations. In addition, it uses a strict false discovery rate control method for variant peptide identifications. The advantages of SAVControl were demonstrated on three colorectal adenocarcinoma cell line datasets and a colorectal cancer dataset. We believe that SAVControl will be a powerful tool for computational proteomics and proteogenomics.