Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection.

BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. METHODS: We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. RESULTS: We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations in TP53, PLXNB3, Zinc finger genes, and fewer mutations in SDC2, CDH1, PIK3CA, SVIL, and PTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA-protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, including KIAA1522, DCK, FOXO3, MYO9B, ARID1A, EPRS, ZC3HAV1, and RBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. CONCLUSIONS: This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Author Correction: Informed-Proteomics: open-source software package for top-down proteomics.

In the version of this article initially published, the authors erroneously reported the search mode that was used for ProSightPC 3.0 in the Online Methods and in Supplementary Table 3.

Exposure to an Environmental Mixture of Polycyclic Aromatic Hydrocarbons Induces Hepatic Cytochrome P450 Enzymes in Mice.

Cytochrome P450 enzymes (CYPs) play an important role in bioactivating or detoxifying polycyclic aromatic hydrocarbons (PAHs), common environmental contaminants. While it is widely accepted that exposure to PAHs induces CYPs, effectively increasing rates of xenobiotic metabolism, dose- and time-response patterns of CYP induction are not well-known. In order to better understand dose- and time-response relationships of individual CYPs following induction, we exposed B6129SF1/J mice to single or repeated doses (2-180 µmol/kg/d) of benzo[a]pyrene (BaP) or Supermix-10, a mixture of the top 10 most abundant PAHs found at the Portland Harbor Superfund Site. In hepatic microsomes from exposed mice, we measured amounts of active CYPs using activity-based protein profiling and total CYP expression using global proteomics. We observed rapid Cyp1a1 induction after 6 h at the lowest PAH exposures and broad induction of many CYPs after 3 daily PAH doses at 72 h following the first dose. Using samples displaying Cyp1a1 induction, we observed significantly higher metabolic affinity for BaP metabolism (Km reduced 3-fold), 3-fold higher intrinsic clearance, but no changes to the Vmax. Mice dosed with the highest PAH exposures exhibited 1.7-5-fold higher intrinsic clearance rates for BaP compared to controls and higher Vmax values indicating greater amounts of enzymes capable of metabolizing BaP. This study demonstrates exposure to PAHs found at superfund sites induces enzymes in dose- and time-dependent patterns in mice. Accounting for specific changes in enzyme profiles, relative rates of PAH bioactivation and detoxification, and resulting risk will help translate internal dosimetry of animal models to humans and improve risk assessments of PAHs at superfund sites.

Phosphoproteome Analysis Reveals Estrogen-ER Pathway as a Modulator of mTOR Activity Via DEPTOR.

ER-positive breast tumors represent ∼70% of all breast cancer cases. Although their treatment with endocrine therapies is effective in the adjuvant or recurrent settings, the development of resistance compromises their effectiveness. The binding of estrogen to ERα, a transcription factor, triggers the regulation of the target genes (genomic pathway). Additionally, a cytoplasmic fraction of estrogen-bound ERα activates oncogenic signaling pathways such as PI3K/AKT/mTOR (nongenomic pathway). The upregulation of the estrogenic and the PI3K/AKT/mTOR signaling pathways are frequently associated with a poor outcome. To better characterize the connection between these two pathways, we performed a phosphoproteome analysis of ER-positive MCF7 breast cancer cells treated with estrogen or estrogen and the mTORC1 inhibitor rapamycin. Many proteins were identified as estrogen-regulated mTORC1 targets and among them, DEPTOR was selected for further characterization. DEPTOR binds to mTOR and inhibits the kinase activity of both mTOR complexes mTORC1 and mTORC2, but mitogen-activated mTOR promotes phosphorylation-mediated DEPTOR degradation. Although estrogen enhances the phosphorylation of DEPTOR by mTORC1, DEPTOR levels increase in estrogen-stimulated cells. We demonstrated that DEPTOR accumulation is the result of estrogen-ERα-mediated transcriptional upregulation of DEPTOR expression. Consequently, the elevated levels of DEPTOR partially counterbalance the estrogen-induced activation of mTORC1 and mTORC2. These results underscore the critical role of estrogen-ERα as a modulator of the PI3K/AKT/mTOR signaling pathway in ER-positive breast cancer cells. Additionally, these studies provide evidence supporting the use of dual PI3K/mTOR or dual mTORC1/2 inhibitors in combination with endocrine therapies as a first-line treatment option for the patients with ER-positive advanced breast cancer.

Informed-Proteomics: open-source software package for top-down proteomics.

Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

Structure Dependent Determination of Organophosphate Targets in Mammalian Tissues Using Activity-Based Protein Profiling.

Acute and chronic exposures to organophosphates (OPs), including agricultural pesticides, industrial chemicals, and chemical warfare agents, remain a significant worldwide health risk. The mechanisms by which OPs alter development and cognition in exposed individuals remain poorly understood, in part due to the large number of structurally diverse OPs and the wide range of affected proteins and signaling pathways. To investigate the influence of structure on OP targets in mammalian systems, we have developed a series of probes for activity-based protein profiling (ABPP) featuring two distinct reactive groups that mimic OP chemical reactivity. FOP features a fluorophosphonate moiety, and PODA and CODA utilize a dialkynyl phosphate ester; both reactive group types target serine hydrolase activity. As the oxon represents the highly reactive and toxic functional group of many OPs, the new probes described herein enhance our understanding of tissue-specific reactivity of OPs. Chemoproteomic analysis of mouse tissues treated with the probes revealed divergent protein profiles, demonstrating the influence of probe structure on protein targeting. These targets also vary in sensitivity toward different OPs. The simultaneous use of multiple probes in ABPP experiments may therefore offer more comprehensive coverage of OP targets; FOP consistently labeled more targets in both brain and liver than PODA or CODA, suggesting the dialkyne warhead is more selective for enzymes in major signaling pathways than the more reactive fluorophosphonate warhead. Additionally, the probes can be used to assess reactivation of OP-inhibited enzymes by N-oximes and may serve as diagnostic tools for screening of therapeutic candidates in a panel of protein targets. These applications will help clarify the short- and long-term effects of OP toxicity beyond acetylcholinesterase inhibition, investigate potential points of convergence for broad spectrum therapeutic development, and support future efforts to screen candidate molecules for efficacy in various model systems.

Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets.

Current mass spectrometry (MS)-based proteomics approaches are ineffective for mapping protein expression in tissue sections with high spatial resolution because of the limited overall sensitivity of conventional workflows. Here we report an integrated and automated method to advance spatially resolved proteomics by seamlessly coupling laser capture microdissection (LCM) with a recently developed nanoliter-scale sample preparation system termed nanoPOTS (Nanodroplet Processing in One pot for Trace Samples). The workflow is enabled by prepopulating nanowells with DMSO, which serves as a sacrificial capture liquid for microdissected tissues. The DMSO droplets efficiently collect laser-pressure catapulted LCM tissues as small as 20 µm in diameter with success rates >87%. We also demonstrate that tissue treatment with DMSO can significantly improve proteome coverage, likely due to its ability to dissolve lipids from tissue and enhance protein extraction efficiency. The LCM-nanoPOTS platform was able to identify 180, 695, and 1827 protein groups on average from 12-µm-thick rat brain cortex tissue sections having diameters of 50, 100, and 200 µm, respectively. We also analyzed 100-µm-diameter sections corresponding to 10-18 cells from three different regions of rat brain and comparatively quantified ∼1000 proteins, demonstrating the potential utility for high-resolution spatially resolved mapping of protein expression in tissues.

A Probe-Enabled Approach for the Selective Isolation and Characterization of Functionally Active Subpopulations in the Gut Microbiome.

Commensal microorganisms in the mammalian gut play important roles in host health and physiology, but a central challenge remains in achieving a detailed mechanistic understanding of specific microbial contributions to host biochemistry. New function-based approaches are needed that analyze gut microbial function at the molecular level by coupling detection and measurements of in situ biochemical activity with identification of the responsible microbes and enzymes. We developed a platform employing ß-glucuronidase selective activity-based probes to detect, isolate, and identify microbial subpopulations in the gut responsible for this xenobiotic metabolism. We find that metabolic activity of gut microbiota can be plastic and that between individuals and during perturbation, phylogenetically disparate populations can provide ß-glucuronidase activity. Our work links biochemical activity with molecular-scale resolution without relying on genomic inference.

Multifunctional Activity-Based Protein Profiling of the Developing Lung.

Lung diseases and disorders are a leading cause of death among infants. Many of these diseases and disorders are caused by premature birth and underdeveloped lungs. In addition to developmentally related disorders, the lungs are exposed to a variety of environmental contaminants and xenobiotics upon birth that can cause breathing issues and are progenitors of cancer. In order to gain a deeper understanding of the developing lung, we applied an activity-based chemoproteomics approach for the functional characterization of the xenometabolizing cytochrome P450 enzymes, active ATP and nucleotide binding enzymes, and serine hydrolases using a suite of activity-based probes (ABPs). We detected P450 activity primarily in the postnatal lung; using our ATP-ABP, we characterized a wide range of ATPases and other active nucleotide- and nucleic acid-binding enzymes involved in multiple facets of cellular metabolism throughout development. ATP-ABP targets include kinases, phosphatases, NAD- and FAD-dependent enzymes, RNA/DNA helicases, and others. The serine hydrolase-targeting probe detected changes in the activities of several proteases during the course of lung development, yielding insights into protein turnover at different stages of development. Select activity-based probe targets were then correlated with RNA in situ hybridization analyses of lung tissue sections.

BRCA2 minor transcript lacking exons 4-7 supports viability in mice and may account for survival of humans with a pathogenic biallelic mutation.

The breast cancer gene, BRCA2, is essential for viability, yet patients with Fanconi anemia-D1 subtype are born alive with biallelic mutations in this gene. The hypomorphic nature of the mutations is believed to support viability, but this is not always apparent. One such mutation is IVS7+2T>G, which causes premature protein truncation due to skipping of exon 7. We previously identified a transcript lacking exons 4-7, which restores the open-reading frame, encodes a DNA repair proficient protein and is expressed in IVS7+2T>G carriers. However, because the exons 4-7 encoded region contains several residues required for normal cell-cycle regulation and cytokinesis, this transcript's ability to support viability can be argued. To address this, we generated a Brca2 knock-in mouse model lacking exons 4-7 and demonstrated that these exons are dispensable for viability as well as tumor-free survival. This study provides the first in vivo evidence of the functional significance of a minor transcript of BRCA2 that can play a major role in the survival of humans who are homozygous for a clearly pathogenic mutation. Our results highlight the importance of assessing protein function restoration by premature truncating codon bypass by alternative splicing when evaluating the functional significance of variants such as nonsense and frame-shift mutations that are assumed to be clearly pathogenic. Our findings will impact not only the assessment of variants that map to this region, but also influence counseling paradigms and treatment options for such mutation carriers.

Proteome Profiling of 1 to 5 Spiked Circulating Tumor Cells Isolated from Whole Blood Using Immunodensity Enrichment, Laser Capture Microdissection, Nanodroplet Sample Processing, and Ultrasensitive nanoLC-MS.

Proteome profiling of circulating tumor cells (CTCs) can provide crucial insight into disease progression and the role of CTCs in tumor metastasis. We describe an integrated workflow to measure global protein expression in 1-5 spiked CTCs enriched from whole blood by immunodensity gradient centrifugation. Enriched CTCs were purified and collected by laser capture microdissection, prepared using a recently developed nanodroplet-based processing platform (nanoPOTS), and finally analyzed by ultrasensitive nanoLC-MS/MS. The workflow was capable of identifying an average of 164 and 607 protein groups from samples comprising 1 and 5 LNCaP cells, respectively, that were isolated from human whole blood. A panel of prostate cancer-specific proteins were identified and quantified, which was used to differentiate between spiked CTCs and white blood cells.

Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease.

Protein modification by O-linked ß-N-acetylglucosamine (O-GlcNAc) is emerging as an important factor in the pathogenesis of sporadic Alzheimer's disease (AD); however, detailed molecular characterization of this important protein post-translational modification at the proteome level has been highly challenging, owing to its low stoichiometry and labile nature. Herein, we report the most comprehensive, quantitative proteomics analysis for protein O-GlcNAcylation in postmortem human brain tissues with and without AD by the use of isobaric tandem mass tag labelling, chemoenzymatic photocleavage enrichment, and liquid chromatography coupled to mass spectrometry. A total of 1850 O-GlcNAc peptides covering 1094 O-GlcNAcylation sites were identified from 530 proteins in the human brain. One hundred and thirty-one O-GlcNAc peptides covering 81 proteins were altered in AD brains as compared with controls (q < 0.05). Moreover, alteration of O-GlcNAc peptide abundance could be attributed more to O-GlcNAcylation level than to protein level changes. The altered O-GlcNAcylated proteins belong to several structural and functional categories, including synaptic proteins, cytoskeleton proteins, and memory-associated proteins. These findings suggest that dysregulation of O-GlcNAcylation of multiple brain proteins may be involved in the development of sporadic AD. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry.

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches.

Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MS.

We report on the quantitative proteomic analysis of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An average of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. We demonstrate that the single-cell proteomics platform can be used to differentiate cell types from enzyme-dissociated human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.

Activity-Based Probes for Isoenzyme- and Site-Specific Functional Characterization of Glutathione S-Transferases.

Glutathione S-transferases (GSTs) comprise a diverse family of phase II drug metabolizing enzymes whose shared function is the conjugation of reduced glutathione (GSH) to endo- and xenobiotics. Although the conglomerate activity of these enzymes can be measured, the isoform-specific contribution to the metabolism of xenobiotics in complex biological samples has not been possible. We have developed two activity-based probes (ABPs) that characterize active GSTs in mammalian tissues. The GST active site is composed of a GSH binding "G site" and a substrate binding "H site". Therefore, we developed (1) a GSH-based photoaffinity probe (GSTABP-G) to target the "G site", and (2) an ABP designed to mimic a substrate molecule and have "H site" activity (GSTABP-H). The GSTABP-G features a photoreactive moiety for UV-induced covalent binding to GSTs and GSH-binding enzymes. The GSTABP-H is a derivative of a known mechanism-based GST inhibitor that binds within the active site and inhibits GST activity. Validation of probe targets and "G" and "H" site specificity was carried out using a series of competition experiments in the liver. Herein, we present robust tools for the characterization of enzyme- and active site-specific GST activity in mammalian model systems.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Diacyltransferase Activity and Chain Length Specificity of Mycobacterium tuberculosis PapA5 in the Synthesis of Alkyl ß-Diol Lipids.

Although they are classified as Gram-positive bacteria, Corynebacterineae possess an asymmetric outer membrane that imparts structural and thereby physiological similarity to more distantly related Gram-negative bacteria. Like lipopolysaccharide in Gram-negative bacteria, lipids in the outer membrane of Corynebacterineae have been associated with the virulence of pathogenic species such as Mycobacterium tuberculosis (Mtb). For example, Mtb strains that lack long, branched-chain alkyl esters known as dimycocerosates (DIMs) are significantly attenuated in model infections. The resultant interest in the biosynthetic pathway of these unusual virulence factors has led to the elucidation of many of the steps leading to the final esterification of the alkyl ß-diol, phthiocerol, with branched-chain fatty acids known as mycocerosates. PapA5 is an acyltransferase implicated in these final reactions. Here, we show that PapA5 is indeed the terminal enzyme in DIM biosynthesis by demonstrating its dual esterification activity and chain-length preference using synthetic alkyl ß-diol substrate analogues. By applying these analogues to a series of PapA5 mutants, we also revise a model for the substrate binding within PapA5. Finally, we demonstrate that the Mtb Ser/Thr kinases PknB and PknE modify PapA5 on three overlapping Thr residues and that a fourth Thr is unique to PknE phosphorylation. These results clarify the DIM biosynthetic pathway and indicate post-translational modifications that warrant further elucidation for their roles in the regulation of DIM biosynthesis.

Disparate proteome responses of pathogenic and nonpathogenic aspergilli to human serum measured by activity-based protein profiling (ABPP).

Aspergillus fumigatus is the primary pathogen causing the devastating pulmonary disease Invasive Aspergillosis in immunocompromised individuals. There is high genomic synteny between A. fumigatus and closely related rarely pathogenic Neosartorya fischeri and Aspergillus clavatus genomes. We applied activity-based protein profiling to compare unique or overexpressed activity-based probe-reactive proteins of all three fungi over time in minimal media growth and in response to human serum. We found 360 probe-reactive proteins exclusive to A. fumigatus, including known virulence associated proteins, and 13 proteins associated with stress response exclusive to A. fumigatus culture in serum. Though the fungi are highly orthologous, A. fumigatus has a significantly greater number of ABP-reactive proteins across varied biological process. Only 50% of expected orthologs of measured A. fumigatus reactive proteins were observed in N. fischeri and A. clavatus. Activity-based protein profiling identified a number of processes that were induced by human serum in A. fumigatus relative to N. fischeri and A. clavatus. These included actin organization and assembly, transport, and fatty acid, cell membrane, and cell wall synthesis. Additionally, signaling proteins regulating vegetative growth, conidiation, and cell wall integrity, required for appropriate cellular response to external stimuli, had higher activity-based probe-protein reaction over time in A. fumigatus and N. fisheri, but not in A. clavatus. Together, we show that measured proteins and physiological processes identified solely or significantly over-represented in A. fumigatus reveal a unique adaptive response to human protein not found in closely related, but rarely pathogenic aspergilli. These unique activity-based probe-protein responses to culture condition may reveal how A. fumigatus initiates pulmonary invasion leading to Invasive Aspergillosis.

Expediting SRM assay development for large-scale targeted proteomics experiments.

Because of its high sensitivity and specificity, selected reaction monitoring (SRM)-based targeted proteomics has become increasingly popular for biological and translational applications. Selection of optimal transitions and optimization of collision energy (CE) are important assay development steps for achieving sensitive detection and accurate quantification; however, these steps can be labor-intensive, especially for large-scale applications. Herein, we explored several options for accelerating SRM assay development evaluated in the context of a relatively large set of 215 synthetic peptide targets. We first showed that HCD fragmentation is very similar to that of CID in triple quadrupole (QQQ) instrumentation and that by selection of the top 6 y fragment ions from HCD spectra, >86% of the top transitions optimized from direct infusion with QQQ instrumentation are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for 3+ precursors and that a significant increase in intensity for transitions could be obtained using a new CE prediction equation constructed from the present experimental data. Overall, our study illustrated the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transition selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.