Data-independent Acquisition Mass Research Articles

Abstract 927: Deep proteomic profiling of CD4+ and CD8+ T cell-depleted tumor models in PD-1 treatment

Abstract Background Immunotherapies targeting the PD-1/PD-L1 axis have been shown to be effective in only around 20% of cancer patients and the mechanism of action (MOA) underlying the differences between responders and non-responders remains poorly understood. It is therefore critical to understand the roles of different lineages of immune cells play mediating PD-1 response, while gaining an overall understanding of the interplay between tumor microenvironment (TME) and immune cell populations. To address this, tumor-bearing syngeneic mice were treated with an anti-PD-1 antibody in combination with targeted CD4+ or CD8+ T cell depletion. Tumor tissues were subsequently investigated with an unbiased proteomics workflow based on data-independent acquisition (DIA) mass spectrometry to provide insights into TME in different treatment contexts. Methods T cell subpopulations were depleted in two subcutaneous murine syngeneic models (MC38, and Hepa 1-6) followed by anti-PD-1 treatment (10mg/kg). The depletion of CD4+ and CD8+ T cells were accomplished with anti-CD4 (GK1.5) and anti-CD8 (2.43). The effectiveness of each depletion was assessed by tumor growth, followed by flow cytometry analysis of tumor infiltrating lymphocytes. FFPE tumor tissue samples, taken at the end of study (17-20 days), were digested using standard procedures and analyzed using 4h gradients on a C18 column coupled to a Thermo Scientific Q Exactive HF-X mass spectrometer. Data were extracted using Spectronaut™ (Biognosys) with a sample specific library. Results Consistent with expectation, depletion of CD8+ T cells significantly attenuated the antitumor effects of anti-PD-1 treatment in both tumor models, confirming the crucial roles of CD8+ T cells in tumor cell killing and inhibition of growth. However, depletion of CD4+ T cells showed opposing effects in the two models tested with a significant enhancement of anti-PD-1 efficacy in MC38 tumors and a reduction of anti-PD-1 efficacy in Hepa1-6 tumors. These effects were further studied by a deep proteome analysis (>9'000 proteins quantified. Statistical analysis showed large differences on proteome level between the two models (>3,000 proteins differentially expressed). We observed a strong upregulation of IFNγ inducible proteins upon anti-PD-1 therapy in the MC38 model, which is boosted upon CD4+ depletion, while many IFNγ pathway related markers were downregulated in the combo group of anti-PD-1 and anti-CD4 in Hepa 1-6. The data of the proteome analysis will be correlated to additional data from RNAseq analysis. Conclusions This study shows an unequivocal role of CD8+ T cells in anti-PD-1 induced tumor growth inhibition whereas the role of CD4+ T cells depends on the specific tumor type and TME. In our study we demonstrate how a deep proteome analysis improves the understanding of the interdependency of immune cells and the TME. Citation Format: Jan Muntel, Ying Jin, Haochen Yu, Nicholas Dupuis, Yongli Shan, Annie X. An, Wubin Qian, Davy Ouyang, Kristina Beeler, Roland Bruderer. Deep proteomic profiling of CD4+ and CD8+ T cell-depleted tumor models in PD-1 treatment [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 927.

Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020.

This review provides a brief overview of the development of data-independent acquisition (DIA) mass spectrometry-based proteomics and selected DIA data analysis tools. Various DIA acquisition schemes for proteomics are summarized first including Shotgun-CID, DIA, MSE , PAcIFIC, AIF, SWATH, MSX, SONAR, WiSIM, BoxCar, Scanning SWATH, diaPASEF, and PulseDIA, as well as the mass spectrometers enabling these methods. Next, the software tools for DIA data analysis are classified into three groups: library-based tools, library-free tools, and statistical validation tools. The approaches are reviewed for generating spectral libraries for six selected library-based DIA data analysis software tools which are tested by the authors, including OpenSWATH, Spectronaut, Skyline, PeakView, DIA-NN, and EncyclopeDIA. An increasing number of library-free DIA data analysis tools are developed including DIA-Umpire, Group-DIA, PECAN, PEAKS, which facilitate identification of novel proteoforms. The authors share their user experience of when to use DIA-MS, and several selected DIA data analysis software tools. Finally, the state of the art DIA mass spectrometry and software tools, and the authors' views of future directions are summarized.

Abstract P6-04-19: Estrogen receptor β agonist S-Equol promotes letrozole sensitivity of endocrine therapy resistant breast cancer cells by upregulating FOXO3 expression

Abstract Background: Breast cancer (BC) is the most frequently diagnosed cancer and the primary leading cause of cancer deaths in women worldwide. Although the clinical outcome of BC patients has been considerably improved, resistance to endocrine and chemotherapy treatments contributes to BC relapse and mortality. Understanding the molecular pathways that contribute to BC resistance will enable to develop new treatments for improving the efficacy of endocrine therapy. Recent studies suggested that estrogen receptor beta (ERβ) and its ligand S-Equol promote suppression of BC progression. However, the mechanisms by which S-equol inhibits growth of BC cells and enhance therapy response remains unknown. Here, we examined the mechanisms by which S-equol contribute to BC growth inhibition in hormone therapy sensitive and resistant BC cells. Methods: We evaluated the therapeutic efficacy of S-equol in therapy sensitive (MCF7-Aro) and letrozole resistant (MCF7-Aro-LTLT) cells. Expression levels of target genes were evaluated in both cell lines by global RNA-seq analysis, data-independent acquisition (DIA) mass spectrometry analyses, qRT-PCR and Western blot. ERβ knock down cell lines were generated by CRISPR-Cas9 technology. Xenograft and patient derived BC explants (PDEX) were used for preclinical evaluation of the antiproliferative activity of S-equol. Results: S-equol reduced the growth of endocrine sensitive and resistant BC models in vitro and in vivo. Treatment of ER-positive PDEX with S-equol decreased the proliferation compared to untreated tumors. RNA-seq analysis of S-equol treated BC cells showed modulation of ERβ target genes involved in tumor progression and resistance to hormone therapies. Mass spectrometry based DIA analysis showed that S-equol treatment upregulated 438 proteins and downregulated 429 proteins in MCF7-Aro cells compared to controls. Whereas, DIA analysis in Letrozole-resistant cells revealed that S-equol upregulated 572 proteins and downregulated 481 proteins compared to controls. Reactome pathway analysis revealed that S-equol treatment modulated the proteins involved in DNA replication, cell cycle, proteins regulating p53 activity and VEGF-VEGFR2 pathway. Our mechanistic studies confirmed that treatment with S-equol upregulated FOXO3 and downregulated FOXM1. Using CRISPR-Cas9 mediated ERβ knockout cells, we confirmed that S-equol could not modulate forkhead box transcription factors in BC cells lacking ERβ compared to parental cells. Conclusions: Collectively, our results suggest that S-equol exhibits its antitumor activity by targeting forkhead box transcription factors and that S-equol might be an effective treatment strategy in treating / preventing breast cancer recurrence. (Supported by BCRP 151884/DOD grant W81XWH-16-1-0294). Citation Format: Kumaraguruparan Ramasamy, Shaorong Chen, Suryavathi Viswanadhapalli, Rong Li, Ratna K Vadlamudi, Rajeshwar R Tekmal. Estrogen receptor β agonist S-Equol promotes letrozole sensitivity of endocrine therapy resistant breast cancer cells by upregulating FOXO3 expression [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-04-19.

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota-Implementation and Computational Analysis.

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory-assembled microbial mixtures as well as human fecal samples.

Abstract 3916: Unbiased phospho-proteomic profiling of mouse breast cancer models with DIA mass spectrometry refines CanPath prototype, a platform for predictive cancer pathway modeling

Abstract Background CanPathPro is designed to build and validate a combined experimental and systems biology platform, which will be used in testing cancer signaling hypotheses. It combines highly defined mouse and organotypic experimental systems, high-dimensional data including next generation sequencing and quantitative proteomics, and computational models for data integration, visualization and modelling. Mouse cancer models are characterized by quantitative transcriptome, quantitative mass spectrometry including phospho-proteome, histopathology and histochemistry. Phospho-proteomic data has been obtained using data independent acquisition (DIA) and used to refine signaling models in the mouse cell lines. Currently, the model integrates modules of the signal transduction pathways Egfr/Erbb2, Fgfr, insulin, Akt, Mtor, Myc, Ras, Hippo, Met, Tgfbr, Il6, integrin, Wnt, apoptosis and cell cycle regulation integrating in total about 380 different genes as well as related proteins and phospho-proteins Methods Two mouse mammary gland model cell lines, (Cdh1-fl/AKT1[E17K] and Cdh1-fl + PTEN-fl) were each treated with DMSO, a Pik3ca inhibitor (Wortmannin), or an Akt inhibitor (MK-2206). Cells were lysed and proteins were denatured, followed by reduction, alkylation and digestion with trypsin. The resulting peptides were desalted and enriched for phosphopeptides with TiO2 beads and cleaned up for mass spectrometry. A phosphopeptide library was generated from pooled phosphoenriched samples using LC-MS/MS shotgun measurements and included 22,893 phosphosites from 3,549 protein groups. DIA data was acquired on a Q Exactive HF mass spectrometer with a gradient length of 60 - 120 minutes on a C18 Easy LC 1200 nano-liquid chromatography system. The DIA data was extracted and processed with Spectronaut 11 (Biognosys) for analysis. Results In the AKT1[E17K] samples, 13,396 peptides (21,862 phospho-peptides) were quantified in the DIA runs and 12,297 peptides (19,928 phospho-peptides) were quantified in PTEN-fl samples. Under treatment with MK2206 and Wortmanin, 859 phosphopeptides from 548 protein groups were significantly changed across all comparisons in the AKT1[E17K] samples and with the same treatments 2,276 phosphopeptides from 976 proteins were significantly changed in the PTEN-fl cells. Based on this data 11 functionally relevant new phosphosites have been added to the model including ones on: EIF4B, FOX03, MAP2K4, PAK1, RAF1, and ULK1. Conclusions Phospho-proteomic profiling of cell lines using DIA mass spectrometry enables deep characterization of the phospho-signaling cascades modulated through small molecule inhibitors. Citation Format: Magdalena Bober, Monika Banko-Bielecka, Daniel Heinzmann, Oliver Rinner, Nicholas Dupuis, Christoph Wierling, Huaibiao Li, Thomas Kessler, Artur Muradyan, Louisa Krützfeldt, Moritz Schütte, Felix Dreher, Aspasia Ploubidou, Bodo Lange. Unbiased phospho-proteomic profiling of mouse breast cancer models with DIA mass spectrometry refines CanPath prototype, a platform for predictive cancer pathway modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3916.

Phosphoregulation Profiles of Transcription Factors during Early Stage of Thermogenesis in Brown Fat

Brown adipose receives extensive attention for dissipating stored chemical energy in heat via nonshivering thermogenesis. However, whether and how the dynamics of key transcription factors (TFs) phosphorylation contributes to initial activation of thermogenesis remains unknown. Here, we employed the stat-of-the-art data-independent acquisition (DIA) mass spectrometry to explore the global and temporal phosphorylation profiles of signaling networks to elucidate mechanism responsible for the TF activation for thermogenesis gene transcription. The nucleus was isolated before and after stimulation for 5, 10, 20, 30, 40, 45, 50, 55, 60, 70 and 80 min with NE. Consequently, 401 transcription regulators bearing 1007 phosphosites were identified with high reproducibility across 12 time points. Firstly, the UCP1 transcriptional level was measured, which began at 30min, then rose until 70min to the peakedness. The global phosphoproteome displayed dramatic changes early to 20-30min, which could be classified into two dynamic clusters, namely UCP1 pre-transcription phase and transcription phase. The former changed from 5min, and reached the extremum at 20-40min, which functioned before UCP1 action. Some well- characterized transcriptional regulators of UCP1 were founded to be phosphorylated, such as PRDM16 and RIP140. Alternatively, other phosphorylation events of TFs such as Jun and ATF2 began from 30min, and maintained until 70min, almost synchronizing with UCP1 transcription. Intriguingly, we found THRAP3 posed multiple phosphosites but involving in different regulation patterns. This might indicate that THRAP3 had distinct transcriptional regulation and functions upon thermogenesis. We, for the first time, uncovered temporal phosphoregulation profiles of TFs, which might be potentially required for initial activation of thermogenesis in brown fat. A new sight was provided to increase energy expenditure for treatment of obesity and other metabolic syndrome. Disclosure X. Gao: None.

Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry

The generation of antigen-specific reagents is a significant bottleneck in the study of complex pathogens that express many hundreds to thousands of different proteins or to emerging or new strains of viruses that display potential pandemic qualities and therefore require rapid investigation. In these instances the development of antibodies for example can be prohibitively expensive to cover the full pathogen proteome, or the lead time may be unacceptably long in urgent cases where new highly pathogenic viral strains may emerge. Because genomic information on such pathogens can be rapidly acquired this opens up avenues using mass spectrometric approaches to study pathogen antigen expression, host responses and for screening the utility of therapeutics. In particular, data-independent acquisition (DIA) modalities on high-resolution mass spectrometers generate spectral information on all components of a complex sample providing depth of coverage hitherto only seen in genomic deep sequencing. The spectral information generated by DIA can be iteratively interrogated for potentially any protein of interest providing both evidence of protein expression and quantitation. Here we apply a solely DIA mass spectrometry based methodology to profile the viral antigen expression in cells infected with vaccinia virus up to 9 h post infection without the need for antigen specific antibodies or other reagents. We demonstrate deep coverage of the vaccinia virus proteome using a SWATH-MS acquisition approach, extracting quantitative kinetics of 100 virus proteins within a single experiment. The results highlight the complexity of vaccinia protein expression, complementing what is known at the transcriptomic level, and provide a valuable resource and technique for future studies of viral infection and replication kinetics. Furthermore, they highlight the utility of DIA and mass spectrometry in the dissection of host-pathogen interactions.