Deep Proteome Research Articles

ALDH4A1 is an atherosclerosis auto-antigen targeted by protective antibodies.

Cardiovascular disease (CVD) is the leading cause of mortality in the world, with most CVD-related deaths resulting from myocardial infarction or stroke. The main underlying cause of thrombosis and cardiovascular events is atherosclerosis, an inflammatory disease that can remain asymptomatic for long periods. There is an urgent need for therapeutic and diagnostic options in this area. Atherosclerotic plaques contain autoantibodies1,2, and there is a connection between atherosclerosis and autoimmunity3. However, the immunogenic trigger and the effects of the autoantibody response during atherosclerosis are not well understood3-5. Here we performed high-throughput single-cell analysis of the atherosclerosis-associated antibody repertoire. Antibody gene sequencing of more than 1,700 B cells from atherogenic Ldlr-/- and control mice identified 56 antibodies expressed by in-vivo-expanded clones of B lymphocytes in the context of atherosclerosis. One-third of the expanded antibodies were reactive against atherosclerotic plaques, indicating that various antigens in the lesion can trigger antibody responses. Deep proteomics analysis identified ALDH4A1, a mitochondrial dehydrogenase involved in proline metabolism, as a target antigen of one of these autoantibodies, A12. ALDH4A1 distribution is altered during atherosclerosis, and circulating ALDH4A1 is increased in mice and humans with atherosclerosis, supporting the potential use of ALDH4A1 as a disease biomarker. Infusion of A12 antibodies into Ldlr-/- mice delayed plaque formation and reduced circulating free cholesterol and LDL, suggesting that anti-ALDH4A1 antibodies can protect against atherosclerosis progression and might have therapeutic potential in CVD.

Integrating human brain proteomes and genome‐wide association results implicates new genes in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common type of dementia, and it has a substantial genetic basis. Recent AD genome‐wide association studies (GWAS) continue to expand the number of loci that are significantly associated with AD; however, how those loci confer risk for AD remains unclear. Here, we addressed this problem by integrating 376 deep human brain proteomes and 888 human brain transcriptomes with recent AD GWAS results to identify genes, transcripts, and proteins that likely contribute to AD pathogenesis.MethodsWe inferred the genetically controlled portion of protein abundance using 376 deep human brain proteomes, and these results were integrated with two recent large AD GWAS results that included 455,258 participants (Jansen et al 2019), and 82,771 participants (Kunkle et al 2019), respectively, using the FUSION pipeline (Gusev et al 2016). We performed an analogous analysis using 888 human brain transcriptomes to infer the genetically controlled portion of the transcript abundance and integrated those results with the aforementioned AD GWAS results.ResultsWe identified a total of 15 genes whose genetically regulated brain protein abundances were associated with AD. They are ACE, CARHSP1, CTSH, DOC2A, EPHX2, ICA1L, LACTB, MADD, PITPNC1, PLEKHA1, PVR, RTFDC1, SNX32, STX4, and STX6. At the transcript level, we identified a total of 49 genes whose cis‐regulated mRNA expression levels were associated with AD. Of the 15 genes identified at the protein level, 33% (5 of 15) were also identified at the transcript level. Furthermore, 73% of these 15 genes identified at the protein level were highly expressed in excitatory neurons based on a reference single‐cell RNA‐sequencing dataset from human brain. In an independent replication dataset using single‐shot label‐free proteomes (n = 205), only one of the 15 proteins were measured and imputed (EPHX2). We replicated EPHX2’s association with AD in that dataset.ConclusionTogether, our findings implicate 15 human brain proteins in the pathogenesis of AD and suggest that these proteins are controlled by both transcriptional and post‐transcriptional mechanisms. Our results lay a foundation for future mechanistic studies to develop novel treatments and biomarkers for AD.

DiaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition.

Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

Comparative Proteomic Analysis of Walnut (Juglans regia L.) Pellicle Tissues Reveals the Regulation of Nut Quality Attributes.

Walnuts (Juglans regia L.) are a valuable dietary source of polyphenols and lipids, with increasing worldwide consumption. California is a major producer, with ’Chandler’ and ’Tulare’ among the cultivars more widely grown. ’Chandler’ produces kernels with extra light color at a higher frequency than other cultivars, gaining preference by growers and consumers. Here we performed a deep comparative proteome analysis of kernel pellicle tissue from these two valued genotypes at three harvest maturities, detecting a total of 4937 J. regia proteins. Late and early maturity stages were compared for each cultivar, revealing many developmental responses common or specific for each cultivar. Top protein biomarkers for each developmental stage were also selected based on larger fold-change differences and lower variance among replicates, including proteins for biosynthesis of lipids and phenols, defense-related proteins and desiccation stress-related proteins. Comparison between the genotypes also revealed the common and specific protein repertoires, totaling 321 pellicle proteins with differential abundance at harvest stage. The proteomics data provides clues on antioxidant, secondary, and hormonal metabolism that could be involved in the loss of quality in the pellicles during processing for commercialization.

Discovery of Candidate Stool Biomarker Proteins for Biliary Atresia Using Proteome Analysis by Data-Independent Acquisition Mass Spectrometry.

Biliary atresia (BA) is a destructive inflammatory obliterative cholangiopathy of the neonate that affects various parts of the bile duct. If early diagnosis followed by Kasai portoenterostomy is not performed, progressive liver cirrhosis frequently leads to liver transplantation in the early stage of life. Therefore, prompt diagnosis is necessary for the rescue of BA patients. However, the prompt diagnosis of BA remains challenging because specific and reliable biomarkers for BA are currently unavailable. In this study, we discovered potential biomarkers for BA using deep proteome analysis by data-independent acquisition mass spectrometry (DIA–MS). Four patients with BA and three patients with neonatal cholestasis of other etiologies (non-BA) were recruited for stool proteome analysis. Among the 2110 host-derived proteins detected in their stools, 49 proteins were significantly higher in patients with BA and 54 proteins were significantly lower. These varying stool protein levels in infants with BA can provide potential biomarkers for BA. As demonstrated in this study, the deep proteome analysis of stools has great potential not only in detecting new stool biomarkers for BA but also in elucidating the pathophysiology of BA and other pediatric diseases, especially in the field of pediatric gastroenterology.

A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).

Deep Profiling of Microgram-Scale Proteome by Tandem Mass Tag Mass Spectrometry.

Tandem mass tag (TMT)-based mass spectrometry (MS) enables deep proteomic profiling of more than 10,000 proteins in complex biological samples but requires up to 100 μg protein in starting materials during a standard analysis. Here, we present a streamlined protocol to quantify more than 9000 proteins with 0.5 μg protein per sample by 16-plex TMT coupled with two-dimensional liquid chromatography and tandem mass spectrometry (LC/LC-MS/MS). In this protocol, we optimized multiple conditions to reduce sample loss, including processing each sample in a single tube to minimize surface adsorption, increasing digestion enzymes to shorten proteolysis and function as carriers, eliminating a desalting step between digestion and TMT labeling, and developing miniaturized basic pH LC for prefractionation. By profiling 16 identical human brain tissue samples of Alzheimer's disease (AD), vascular dementia (VaD), and non-dementia controls, we directly compared this new microgram-scale protocol to the standard-scale protocol, quantifying 9116 and 10,869 proteins, respectively. Importantly, bioinformatics analysis indicated that the microgram-scale protocol had adequate sensitivity and reproducibility to detect differentially expressed proteins in disease-related pathways. Thus, this newly developed protocol is of general application for deep proteomics analysis of biological and clinical samples at sub-microgram levels.

Deep proteomics and phosphoproteomics reveal novel biological pathways perturbed by morphine, morphine-3-glucuronide and morphine-6-glucuronide in human astrocytes.

Tolerance and hyperalgesia associated with chronic exposure to morphine are major limitations in the clinical management of chronic pain. At a cellular level, neuronal signaling can in part account for these undesired side effects, but unknown mechanisms mediated by central nervous system glial cells are likely also involved. Here we applied data-independent acquisition mass spectrometry to perform a deep proteome and phosphoproteome analysis of how human astrocytes responds to opioid stimulation. We unveil time- and dose-dependent effects induced by morphine and its major active metabolites morphine-3-glucuronide (M3G) and morphine-6-glucuronide that converging on activation of mitogen-activated protein kinase and mammalian target of rapamycin signaling pathways. We also find that especially longer exposure to M3G leads to significant dysregulation of biological pathways linked to extracellular matrix organization, antigen presentation, cell adhesion, and glutamate homeostasis, which are crucial for neuron- and leukocyte-astrocyte interactions.

Development of an Online 2D Ultrahigh-Pressure Nano-LC System for High-pH and Low-pH Reversed Phase Separation in Top-Down Proteomics.

The development of novel high-resolution separation techniques is crucial for advancing the complex sample analysis necessary for high-throughput top-down proteomics. Recently, our group developed an offline 2D high-pH RPLC/low-pH RPLC separation method and demonstrated good orthogonality between these two RPLC formats. Specifically, ultrahigh-pressure long capillary column RPLC separation has been applied as the second dimensional low-pH RPLC separation for the improvement of separation resolution. To further improve the throughput and sensitivity of the offline approach, we developed an online 2D ultrahigh-pressure nano-LC system for high-pH and low-pH RPLC separations in top-down proteomics. An online microtrap column with a dilution setup was used to collect eluted proteins from the first dimension high-pH separation and inject the fractions for ultrahigh-pressure long capillary column low-pH RPLC separation in the second dimension. This automatic platform enables the characterization of 1000+ intact proteoforms from 5 μg of intact E. coli cell lysate in 10 online-collected fractions. Here, we have demonstrated that our online 2D pH RP/RPLC system coupled with top-down proteomics holds the potential for deep proteome characterization of mass-limited samples because it allows the identification of hundreds of intact proteoforms from complex biological samples at low microgram sample amounts.

Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate Proteome Profiling.

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micropillar array columns, as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4% were obtained even at only 1.5 data points per peak (full width at half-maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology, and molecular biology.

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

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.

Abstract 2848: Analytical validation of the multi-nanoparticle Proteograph platform for rapid and deep proteomic profiling

Abstract Introduction: Plasma proteins should be useful biomarkers for disease detection, yet few proteins (~120) are FDA approved. Productive biomarker discovery studies are resource-limited due to the complex biochemical fractionation methods used to address the inherent challenges in unbiased plasma profiling such as the large dynamic range. Herein, we describe Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles (NPs) for deep and unbiased proteomic sampling. NPs reproducibly bind subsets of proteins from biofluids as a function of protein concentration, protein-NP affinity, and protein-protein interactions to form a corona on the NP surface. The corona composition is directly a function of the NPs' biophysicochemical properties and requires no prior knowledge of the proteins that might be selected by each distinctly engineered NP. With an optimized panel of 10 NPs, we can broadly and deeply interrogate the plasma proteome and rapidly quantify potential biomarkers. The highly parallel NP workflow makes large studies practicable and should improve biomarker discovery and validation. Methods and Results: We have screened 200+ NPs with different biophysicochemical properties and selected a panel of 10 based on protein detection. A fully automated assay workflow was developed that can process the 10 NP panel across 8 plasma samples in a 7 hr assay. Evaluation of this panel across 16 individual plasma samples detected 2,009 protein groups (1% protein FDR, 84% with 2 or more peptides). Accuracy was demonstrated with spike-recovery experiments using 4 NPs in which CRP, Angiogenin and S100a8/9 were added to plasma at 2X, 5X, 10X, and 100X of measured endogenous levels. Linear model fits for NP corona MS signal vs. ELISA were created with mean slopes of 1.06 ± 0.22 and mean adjusted-r2 of 0.95 ± 0.05. Precision was demonstrated across 3 NPs using three assay replicates in which the mean of the median CVs for each NP is 24%. The depth of plasma proteome coverage for the 10 NP panel using a pooled plasma sample was determined by comparison of the NP-detected proteins to published MS intensities and spanned nearly the entire reported range. Examining protein annotations (e.g., GO Cellular Compartment and Biological Process, KEGG and Pfam) within each NP corona reveals correlations by 1D-enrichment analysis between protein annotations and NP biophysicochemical properties suggesting specific relationships at the nano-bio surface. Discussion: We have demonstrated the selection and optimization of a panel of 10 NPs for plasma proteome profiling. We have also demonstrated the breadth and depth of this panel's ability to accurately and precisely quantify proteins from plasma. We believe the robustness and scalability of the Proteograph platform could enable population-scale deep and unbiased proteomics analysis previously not feasible using existing workflows. Citation Format: John E. Blume, William C. Manning, Gregory Troiano, Asim Siddiqui, Philip Ma, Robert Langer, Vivek Farias, Omid C. Farokhzad. Analytical validation of the multi-nanoparticle Proteograph platform for rapid and deep proteomic profiling [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 2848.

Abstract LB-088: A STAT3 selective targeted protein degrader decreases the immunesuppressive tumor microenvironment and drives antitumor activity in preclinical models

Abstract Signal transducer and activator of transcription 3 (STAT3) has been implicated in multiple aspects of tumorigenesis. In addition to increasing cancer cell proliferation and survival, constitutively activated STAT3 is proposed to regulate cross-talk between tumor, stroma and immune cells to promote immune-evasion. STAT3 activity in tumors promotes the production of immune-suppressive factors that activate STAT3 in diverse immune-cell subsets. Mechanistically, genetic studies support a direct role of activated STAT3 in regulating myeloid cell differentiation to contribute to an immune-suppressed tumor microenvironment (TME) (Kortylewski et al.; Nat. Med. 2005 and Curr. Opin. Immunol. 2008) Therefore, STAT3 is a highly attractive target for immune-oncology. Here, we demonstrate that the degradation of STAT3 with a potent and selective STAT3 degrader reverses immune suppression in preclinical models. KYM-003 is a heterobifunctional molecule that hijacks the ubiquitin-proteasome system (UPS) for rapid STAT3 degradation. KYM-003 robustly degraded STAT3 in both human peripheral blood mononuclear cells (hPBMC)-derived monocytes and lymphocytes with DC50 < 100 nM. Degradation in hPBMCs was highly selective for STAT3 vs >10,000 other detected proteins (including all other STAT family members) as evaluated by deep tandem mass tag proteomics. IL-6 treatment of hPBMCs upregulates STAT3 phosphorylation, resulting in transcription of genes involved in myeloid cell-mediated immune suppression, such as IL-10 and CD163, a marker of M2 macrophage differentiation. Treatment of cells with KYM-003 at DC90 for 6 hours abrogated the IL-6 induced up-regulation of immune-suppressive gene signatures consistent with a role of STAT3 in mediating an immune suppressive environment by regulating macrophages and other myeloid cells. In several tumor cell lines, degradation of STAT3 by KYM-003 led to strong downregulation of PD-L1. Importantly, in the BioMap StroNSCLC (DiscoverRx), a co-culture system composed of NCI-H1299 lung cancer cells, hPBMCs and primary human fibroblasts that models immune-suppressed TME biology, KYM-003 treatment decreased angiogenic and immune-suppressive cytokines, including VEGF and IL-10, and promoted the pro-inflammatory anti-tumor cytokines IL-2, IFNg and TNFa. Lastly, administration of KYM-003 to mice bearing syngeneic tumors exhibited anti-tumor activity as monotherapy in the CT26 syngeneic model. We have shown that targeting STAT3 for degradation may have a role in restoring an immune-permissive environment in tumors by both rescuing the suppressed immunologic microenvironment and directly downregulating immune checkpoint signals in tumor cells. These data support STAT3 degraders as a promising new therapeutic modality as immune-oncology agents.Citations: Kortylewski et al (2005) Nat. Med, 11(12):1314-21. Kortylewski et al (2008) Curr. Opin. Immunol, 20(2):228-33 Citation Format: Alfredo Csibi, Bin Yang, Yogesh Chutake, Karen Yuan, Michele Mayo, Veronica Campbell, Alice McDonald, Scott Rusin, Kirti Sharma, Hari Kamadurai, Henry Li, Mike Sintchak, Sean Zhu, Sharon Townson, Anthony Slavin, Haojing Rong, Phillip Liu, Chris De Savi, Jared Gollob, Duncan Walker, Nan Ji, Nello Mainolfi. A STAT3 selective targeted protein degrader decreases the immunesuppressive tumor microenvironment and drives antitumor activity in preclinical models [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 LB-088.

Abstract 4077: Quantitative proteomics reveals novel immunomodulatory pathways of resistance to PARP therapy

Abstract Background Pharmacological inhibition of PARP results in the specific killing of BRCA1/2 deficient tumor cells due to a synthetic lethal interaction between the concomitant impairment in homologous recombination and the DNA damage response. Despite the success of this approach, resistance to PARP inhibition has been observed in majority of patients with advanced cancer. Novel ways of interrogating PARP resistance are necessary to further elucidate the mechanisms of drug resistance and help identify ways to overcome it. To probe potential mechanisms of resistance we applied data-independent acquisition (DIA) mass spectrometry for unbiased global protein quantification in patient derived xenografts with demonstrated resistance to PARP inhibitors. Methods Patient-derived tumor xenografts (PDTXs) were generated from breast cancer patient tumor material implanted in severely immuno-compromised NOD-scid IL2rgnull (NSG) mice. PDTXs, developed as part of the CRUK Cambridge Institute biobank of PDTXs, have previously undergone extensive molecular profiling. PDTXs and short-term cultured PDTX cells (or PDTCs) capture most of originating patient's sample features, including heterogeneity, and consequently, the PDTX/PDTC platform is a robust intermediate in oncogenic drug development. Resistance to the PARP inhibitors AZD-2281 (olaparib) and BMN-673 (talazoparib) was tested ex vivo in PDTCs from 6 sensitive and 11 resistant PDTXs. Deep quantitative proteome profiling of PDTXs samples was conducted by applying an LC-MS/MS setup operated in DIA mode. Data was extracted using Spectronaut™ (Biognosys) with a sample specific spectral library. Pathway analysis was conducted to highlight dysregulated biological functions and pathways and the proteomics data was correlated with transcriptomics readouts. Results LC-MS/MS profiling allowed the identification of more than 11'000 proteins with more than 8'700 proteins quantified across the samples. 448 human and 430 murine proteins were significantly changed between PARP inhibitor resistant and sensitive PDTX models. Resistant models were characterized by increased expression of DNA damage response proteins including ATR and FANCD2, downregulation of TP53, TP53BP1, POLB and H2AFX, and upregulated EGFR, BRAF, ERBB2, STAT3, MLLT4 and CDKN2A. Most interestingly, we observed upregulation of human CD47/SIRPa immunomodulatory signals and upregulation of mouse Ly6G, CSF1R and SHP-1 suggesting the infiltration of immunosuppressive neutrophils and monocytes in the tumor microenvironment in the models resistant to PARP inhibition. Conclusions To our knowledge, this is the first report aiming at interrogating PARP inhibitor resistance with unbiased quantitative proteomics. The proteomics data confirmed prior observations of resistance mechanisms to PARP and elucidated potential novel mechanisms involving modulation of the immune response in resistant tumors. Citation Format: Jakob Vowinckel, Yuehan Feng, Dimitra Georgopoulou, Alejandra Bruna, Tobias Treiber, Abigail Shea, Giulia Lerda, Martin O'Reilly, Kristina Beeler, Carlos Caldas. Quantitative proteomics reveals novel immunomodulatory pathways of resistance to PARP therapy [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 4077.

Descriptive Proteome Analysis to Investigate Context-Dependent Treatment Responses to OXPHOS Inhibition in Colon Carcinoma Cells Grown as Monolayer and Multicellular Tumor Spheroids.

We have previously identified selective upregulation of the mevalonate pathway genes upon inhibition of oxidative phosphorylation (OXPHOS) in quiescent cancer cells. Using mass spectrometry-based proteomics, we here investigated whether these responses are corroborated on the protein level and whether proteomics could yield unique insights into context-dependent biology. HCT116 colon carcinoma cells were cultured as monolayer cultures, proliferative multicellular tumor spheroids (P-MCTS), or quiescent (Q-MCTS) multicellular tumor spheroids and exposed to OXPHOS inhibitors: nitazoxanide, FCCP, oligomycin, and salinomycin or the HMG-CoA-reductase inhibitor simvastatin at two different doses for 6 and 24 h. Samples were processed using an in-depth bottom-up proteomics workflow resulting in a total of 9286 identified protein groups. Gene set enrichment analysis showed profound differences between the three cell systems and confirmed differential enrichment of hypoxia, OXPHOS, and cell cycle progression-related protein responses in P-MCTS and Q-MCTS. Treatment experiments showed that the observed drug-induced alterations in gene expression of metabolically challenged cells are not translated directly to the protein level, but the results reaffirmed OXPHOS as a selective vulnerability of quiescent cancer cells. This work provides rationale for the use of deep proteome profiling to identify context-dependent treatment responses and encourages further studies investigating metabolic processes that could be co-targeted together with OXPHOS to eradicate quiescent cancer cells.

Bioinformatic and experimental evidence for suicidal and catalytic plant THI4s.

Like fungi and some prokaryotes, plants use a thiazole synthase (THI4) to make the thiazole precursor of thiamin. Fungal THI4s are suicide enzymes that destroy an essential active-site Cys residue to obtain the sulfur atom needed for thiazole formation. In contrast, certain prokaryotic THI4s have no active-site Cys, use sulfide as sulfur donor, and are truly catalytic. The presence of a conserved active-site Cys in plant THI4s and other indirect evidence implies that they are suicidal. To confirm this, we complemented the Arabidopsistz-1 mutant, which lacks THI4 activity, with a His-tagged Arabidopsis THI4 construct. LC-MS analysis of tryptic peptides of the THI4 extracted from leaves showed that the active-site Cys was predominantly in desulfurated form, consistent with THI4 having a suicide mechanism in planta. Unexpectedly, transcriptome data mining and deep proteome profiling showed that barley, wheat, and oat have both a widely expressed canonical THI4 with an active-site Cys, and a THI4-like paralog (non-Cys THI4) that has no active-site Cys and is the major type of THI4 in developing grains. Transcriptomic evidence also indicated that barley, wheat, and oat grains synthesize thiamin de novo, implying that their non-Cys THI4s synthesize thiazole. Structure modeling supported this inference, as did demonstration that non-Cys THI4s have significant capacity to complement thiazole auxotrophy in Escherichia coli. There is thus a prima facie case that non-Cys cereal THI4s, like their prokaryotic counterparts, are catalytic thiazole synthases. Bioenergetic calculations show that, relative to suicide THI4s, such enzymes could save substantial energy during the grain-filling period.

Maspin as a Prognostic Marker for Early Stage Colorectal Cancer With Microsatellite Instability.

Colorectal cancers are among the most common cancers and a leading cause of cancer death. In our pursuit to discover molecular markers for better characterization and precision theranostics of these cancers, we first conducted global deep proteome analyses and identified maspin (serpin B5, peptidase inhibitor 5) as an upregulated protein in tumor tissue. We then validated its expression in a large cohort of 743 patients with colorectal cancers of all stages and found that both cytoplasmic and nuclear expression varied widely between different patients. Comparison with clinicopathological features revealed that maspin expression levels correlate significantly only with mismatch repair (MMR) status but not with other features. To elucidate the prognostic significance of maspin, we analyzed two outcome-annotated cohorts, one of 572 early stage cancer patients and another of 93 late stage cancer patients. Kaplan–Meier survival, univariate, and multivariate analyses revealed that maspin overexpression predicts longer overall and disease-free survival for early stage microsatellite instability (MSI) subtype colorectal cancer, but there is no correlation with survival for patients with early stage cancer of the microsatellite stability (MSS) subtype or late stage cancer. Our study identifies maspin expression as an independent prognostic marker for risk stratification of early stage MSI subtype colorectal cancer and may provide guidance for improved therapeutic management.

AutoProteome Chip System for Fully Automated and Integrated Proteomics Sample Preparation and Peptide Fractionation.

With recent advances in LC-MS systems, current MS-based proteomics has an increasing need for automated, high-throughput sample preparation with neglectable sample loss. In this study, we developed a microfluidic system for fully automated proteomics sample preparation. All of the required proteomics sample preparation steps for both protein digestion and peptide fractionation are fully integrated into a disposable plastic chip device (named AutoProteome Chip). The AutoProteome Chip packed with mixed-mode ion exchange beads and C18 membrane in tandem could be fabricated with very low cost and high stability in organic reagents. Benefiting from its low backpressure, the AutoProteome Chip could be precisely driven by gas pressure, which could be easily multiplexed. As low as 2 ng of standard protein BSA could be trapped into the AutoProteome chip and processed within 2 h. Fully automated processing of 10 μg of protein extracts of HEK 293T cells achieved more than 97% of digestion efficiency with missed cleavage less than 2 and comparable performance with conventional approaches. More than 4700 proteins could be readily identified within 80 min of LC-MS analysis with good label-free quantification performance (Pearson correlation coefficient >0.99). Furthermore, deep proteome profiling by integrated high-pH RP fractionation in the same AutoProteome Chip resulted in more than 7500 proteins being identified from only 20 μg of protein extracts of HEK 293T cells and comparable reprodicibility as single-shot analysis. The AutoProteome Chip system provided a valuable prototype for developing a fully automated proteome analysis workflow and for proteomic applications with high demand for processing throughput, reproducibility, and sensitivity.

Biomarkers of tumor invasiveness in proteomics (Review).

Over the past two decades, quantitative proteomics has emerged as an important tool for deciphering the complex molecular events involved in cancers. The number of references involving studies on the cancer metastatic process has doubled since 2010, while the last 5 years have seen the development of novel technologies combining deep proteome coverage capabilities with quantitative consistency and accuracy. To highlight key findings within this huge amount of information, the present review identified a list of tumor invasive biomarkers based on both the literature and data collected on a biocollection of experimental cell lines, tumor models of increasing invasiveness and tumor samples from patients with colorectal or breast cancer. Crossing these different data sources led to 76 proteins of interest out of 1,245 mentioned in the literature. Information on these proteins can potentially be translated into clinical prospects, since they represent potential targets for the development and evaluation of innovative therapies, alone or in combination. Herein, a systematical review of the biology of each of these proteins, including their specific subcellular/extracellular or multiple localizations is presented. Finally, as an important advantage of quantitative proteomics is the ability to provide data on all these molecules simultaneously in cell pellets, body fluids or paraffin-embedded sections of tumors/invaded tissues, the significance of some of their interconnections is discussed.