Elovanoid-N34 modulates TXNRD1 key in protection against oxidative stress-related diseases

The thioredoxin (TXN) system is an NADPH + H+/FAD redox-triggered effector that sustains homeostasis, bioenergetics, detoxifying drug networks, and cell survival in oxidative stress-related diseases. Elovanoid (ELV)-N34 is an endogenously formed lipid mediator in neural cells from omega-3 fatty acid precursors that modulate neuroinflammation and senescence gene programming when reduction-oxidation (redox) homeostasis is disrupted, enhancing cell survival. Limited proteolysis (LiP) screening of human retinal pigment epithelial (RPE) cells identified TXNRD1 isoforms 2, 3, or 5, the reductase of the TXN system, as an intracellular target of ELV-N34. TXNRD1 silencing confirmed that the ELV-N34 target was isoform 2 or 3. This lipid mediator induces TXNRD1 structure changes that modify the FAD interface domain, leading to its activity modulation. The addition of ELV-N34 decreased membrane and cytosolic TXNRD1 activity, suggesting localizations for the targeted reductase. These results show for the first time that the lipid mediator ELV-N34 directly modulates TXNRD1 activity, underling its protection in several pathologies when uncompensated oxidative stress (UOS) evolves.

The atrial and ventricular myocardial proteome of end-stage lamin heart disease

Lamins A/C (encoded by LMNA gene) can lead to dilated cardiomyopathy (DCM). This pilot study sought to explore the postgenomic phenotype of end-stage lamin heart disease. Consecutive patients with end-stage lamin heart disease (LMNA-group, n = 7) and ischaemic DCM (ICM-group, n = 7) undergoing heart transplantation were prospectively enrolled. Samples were obtained from left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV) and interventricular septum (IVS), avoiding the infarcted myocardial segments in the ICM-group. Samples were analysed using a discovery 'shotgun' proteomics approach. We found that 990 proteins were differentially abundant between LMNA and ICM samples with the LA being most perturbed (16-fold more than the LV). Abundance of lamin A/C protein was reduced, but lamin B increased in LMNA LA/RA tissue compared to ICM, but not in LV/RV. Carbonic anhydrase 3 (CA3) was over-abundant across all LMNA tissue samples (LA, LV, RA, RV, and IVS) when compared to ICM. Transthyretin was more abundant in the LV/RV of LMNA compared to ICM, while sarcomeric proteins such as titin and cardiac alpha-cardiac myosin heavy chain were generally less abundant in RA/LA of LMNA. Protein expression profiling and enrichment analysis pointed towards sarcopenia, extracellular matrix remodeling, deficient myocardial energetics, redox imbalances, and abnormal calcium handling in LMNA samples. Compared to ICM, end-stage lamin heart disease is a biventricular but especially a biatrial disease appearing to have an abundance of lamin B, CA3 and transthyretin, potentially hinting to compensatory responses.

Abstract 3974: Discovery and validation of therapeutic targets in immune cells by mass spectrometry-based proteomics

Immuno-oncology (IO) has substantially improved the survival of cancer patients over the past several years encouraging the discovery of novel IO targets which are typically proteins expressed on the surface of immune cells. Sensitive quantification of proteins in complex biological samples is routinely achieved by immunoassays that use antibodies specific to target proteins. Such approaches can be a limitation in IO drug discovery and development as de novo development of antibodies is associated with long lead times, high costs, and high failure rates.

Protein quantification using mass spectrometry (MS) is agnostic to species and matrices and removes the barriers of availability or specificity of antibody-based assay. Further, MS proteomics workflows can support both large scale discovery studies but also represent an attractive alternative to targeted quantitative studies.

The main purpose of this work is to assess the performance of the TrueDiscovery™ and TrueSignature™ MS-proteomics platforms for the deep proteome and surfaceome profiling of human primary and immortalized immune cells compared to flow cytometry solutions. We assess the number of quantified proteins and specifically the coverage of immune cell marker proteins in primary human immune cells across cell count groups from 2 million down to 2500 immune cells. In addition, we compared the quantification of a multiplexed surface antigens panel using TrueSignature™ and QIFI® flow cytometry platforms.

We found that the applied MS-based proteomics workflows achieve high sensitivity and robustness in detection and quantification of immune cell markers down to 2500 primary immune cells. Additionally, we observed a strong correlation of the quantitative data derived from our MS-based proteomics workflows with flow cytometry supporting the substitution of immunoassays by MS-based proteomics workflows in target discovery and validation.

Citation Format: Yu-Wah Au, David Satijn, Martin Mehnert, Amaury Lachaud, Yuehan Feng, Jakob Vowinckel, Hélène Bon. Discovery and validation of therapeutic targets in immune cells by mass spectrometry-based proteomics. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3974.

343 Multiomic biomarker signatures identify subsets of patients who may benefit from either nivolumab or sotigalimab in combination with chemotherapy in metastatic pancreatic cancer

Background

Gemcitabine/nab-Paclitaxel (GnP) is a standard of care regimen for first-line metastatic pancreatic ductal adenocarcinoma (PDAC) and has a 1-year overall survival (OS) rate of approximately 35%. There is an urgent need for novel therapeutics and precision medicine approaches in PDAC. PRINCE, a randomized phase 2 trial, reported an increased 1-year OS relative to historical data, for patients treated with nivolumab (nivo)/GnP (57.3%, p = 0.007, n=34) and sotigalimab (sotiga) (APX005M; CD40 agonist)/GnP (48.1%, p = 0.062, n= 36).

Methods

To investigate immune modulatory and pharmacodynamic (PD) effects of nivo or sotiga in combination with GnP we used several orthogonal minimally invasive, blood-based biomarker technologies. Immune population profiles were evaluated by CyTOF and features of T cell phenotype and function by multicolor flow cytometry. Soluble proteins were evaluated with predefined panels using the Olink platform (Immuno-oncology (IO) and Immune Response) along with an unbiased mass spectrometry proteomic approach (Biognosys) that identified circulating soluble proteins of significance.

Results

Relative to baseline, patients who received nivo/GnP had numerically increased frequencies of proliferating, activated CD8+ and CD4+ effector memory T cells in circulation across multiple timepoints. These patients also had significantly increased levels of soluble proteins associated with type II interferon responses and immune cell migration and T cell activation, as well as significantly decreased levels of immunomodulatory proteins.Patients who received sotiga/GnP had increased expression of the co-stimulatory molecule CD86 on conventional dendritic cells. These patients also had significantly increased concentrations of soluble proteins associated with mature antigen presenting cells, and the activation of helper CD4+ T cells, B cells, and monocytes. Significant increases in soluble proteins associated with type-1 cell-mediated effector immunity and decreases in immunosuppressive factors were observed in both arms. Significant proteins were defined as p ≤ 0.05, log2 expression fold change ≥ 0.5 (Olink) and Sparse PLS discriminant analysis was used with zero as a threshold (Biognosys).

Conclusions

This study is a first to use multiomic minimally invasive biomarker approaches in PDAC to demonstrate PD effects and immune modulation with immunotherapy/chemotherapy combinations. Orthogonal assays demonstrate that nivo/GnP and sotiga/GnP elicit unique immune responses and the observed effects are consistent with their distinct mechanisms of action. These data suggest that multiomic biomarker signatures may identify subsets of patients who may benefit from an immunotherapy/chemo approach in PDAC. Moreover, results from these analyses will support early phase clinical study development decisions.

Acknowledgements

We extend our gratitude to the patients, their families, the clinical investigators, and their site staff members who are making this trial possible. We would also like to thank Sultan Nawabi at Parker Institute for Cancer Immunotherapy (PICI) for operations leadership of the trial. We thank Bristol Myers Squibb (BMS) and Apexigen for collaboration and study drugs. The study was funded by Cancer Research Institute, BMS and PICI.

Trial Registration

NCT03214250

Ethics Approval

This study was approved by University of Pennsylvania Institutional Review Board; Federalwide assurance #00004028.

The metabolic growth limitations of petite cells lacking the mitochondrial genome

Eukaryotic cells can survive the loss of their mitochondrial genome, but consequently suffer from severe growth defects. 'Petite yeasts', characterized by mitochondrial genome loss, are instrumental for studying mitochondrial function and physiology. However, the molecular cause of their reduced growth rate remains an open question. Here we show that petite cells suffer from an insufficient capacity to synthesize glutamate, glutamine, leucine and arginine, negatively impacting their growth. Using a combination of molecular genetics and omics approaches, we demonstrate the evolution of fast growth overcomes these amino acid deficiencies, by alleviating a perturbation in mitochondrial iron metabolism and by restoring a defect in the mitochondrial tricarboxylic acid cycle, caused by aconitase inhibition. Our results hence explain the slow growth of mitochondrial genome-deficient cells with a partial auxotrophy in four amino acids that results from distorted iron metabolism and an inhibited tricarboxylic acid cycle.

Abstract 1623: Response and skin toxicity related protein signature in late stage melanoma patients after anti-PD-1 treatment

Skin toxicity after anti-PD1 treatment in melanoma patients is the most common type of immune related adverse effect (irAE) and has been associated with improved overall response rate and survival. Nonetheless, not many mechanistic biomarkers have been identified so far, that could be associated with low-grade skin toxicity and good response rates. In this study we have addressed this question by analyzing tumor tissue samples from late-stage melanoma patients with first-line anti-PD1 treatment. Samples obtained prior to treatment were submitted for an unbiased deep proteomic analysis using mass spectrometry (LC-MS) and a targeted transcriptomic analysis. Using the unbiased analysis as a discovery platform we were able to define a potential biomarker panel associated not only with improved response but also low-grade skin toxicity. Unbiased quantification of proteins in tumor tissues was done using data-independent acquisition (DIA) LC-MS technology. Proteins from tissue samples were denatured, digested, and analyzed on a mass spectrometer. A deep spectral library was generated, and proteins were quantified using Spectronaut software (Biognosys). In addition, from the same tumor tissue RNA was extracted and subjected to transcriptomic analysis with NanoString nCounter using the PanCancer IO 360 panel. Subsequent data analysis was done using a sPLS-DA using combined factor of skin toxicity and response. Unbiased analysis of 22 baseline tumor tissue samples from late-stage melanoma patients treated in first line with anti-PD1 resulted in identification and quantification of more than 8000 proteins. Progression free survival analysis showed difference between patients with reported low-grade skin toxicity against all others. Therefore, for sPLS-DA both factors, presence/absence of skin toxicity and response status, were used (non-responders with low-grade skin toxicity were not present in this cohort). Complete separation of subjects was achieved with a panel of 21 proteins. This panel was used for hierarchical clustering and was able to fully restore all three groups of patients. Among all proteins identified in the proteomic panel, melanoma-associated antigen C1 (MAGEC1) has been also assessed in the targeted transcriptomic analysis and represents strikingly similar results. MAGEC1 is also found as a strong predictor in the Human Protein Atlas project. Interestingly, MAGE protein family are tumor-specific antigens that can be recognized by autologous cytolytic T lymphocytes and could serve as a novel ICI target or predictive biomarker. In this study we confirm prior observations of a survival benefit related to irAEs after treatment with PD-1 blockade in late-stage melanoma patients. We also demonstrate the power of deep proteomic profiling and transcriptomic analysis in molecular biomarker selection associated to response and irAEs which further benefit patient survival.

Citation Format: Jakob Vowinckel, Domenico Mallardo, Kamil Sklodowski, Martin Soste, Mariaelena Capone, Gabriele Madonna, Vito Vanella, Sarah Warren, Kristina Beeler, Paolo A. Ascierto. Response and skin toxicity related protein signature in late stage melanoma patients after anti-PD-1 treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1623.

Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity

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Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

Proteomics meets transcriptomics: Identification of tumor tissue signatures specific to anti-PD1 treatment in late-stage melanoma patients

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Background: Despite advances of anti-PD1 treatment in melanoma, still large subset of patients does not respond or relapses due to primary or acquired resistance. One potential way to overcome the mechanisms of resistance is to identify molecular signatures associated with response to treatment. Here, we are presenting the results of an integrated deep transcriptomic and proteomic analysis of melanoma tissue samples coming from patients prior the treatment with anti-PD1. The combination of targeted transcriptomic approach with unbiased proteomic approach allowed for identification of a molecular response signature specific to anti-PD1 therapy. Methods: Unbiased quantification of proteins in tumor tissues was done using data-independent acquisition (DIA) LC-MS technology. Proteins from tissue samples were denatured, digested, and analyzed on a mass spectrometer. A deep spectral library was generated, and proteins were quantified using Spectronaut software (Biognosys). From the same tumor tissue RNA was extracted and subjected to transcriptomic analysis with NanoString nCounter using the PanCancer IO 360 panel. Integration analysis using latent components, a generalized PLS and sparse sGCCA method implemented in R mixOmics package was used for signature discovery. Results: Studied cohort was balanced for gender, BRAF mutations and stage. Treatment included Nivolumab or Pembrolizumab. Only among responding subjects, low grade (≤ 2) skin toxicity was identified at a significant level (p-value < 0.05). In total, 22 samples were measured (nine non-responders and 13 responders). Overall, the analysis of proteome across all samples resulted in 8548 proteins being identified and quantified. The IO 360 panel contained 770 targets. In combined analysis, 10 mRNA targets together with 64 protein targets were highly associated with response to treatment. Two top candidates identified for mRNA and proteins were SIGLEC5 and ACP6 respectively. The panel of 74 features was sufficient to separate all subjects using unsupervised hierarchical clustering into to two main clusters enriched for responders and non-responders (p-value < 0.05). String-DB analysis revealed numerous interactions and associations within identified panel. Functional analysis using GO enrichment showed major involvement of the selected mRNA and proteins in T-cell regulation as well as in neutrophil degranulation and antigen receptor mediated signaling. Conclusions: Combination of both omics assays provides a very comprehensive image of tumor tissue responses to anti-PD1 treatment in late-stage melanoma patients. Identified candidates show striking changes in responder and non-responder groups and should undergo further validation for use in precision medicine.

Slow Growth and Increased Spontaneous Mutation Frequency in Respiratory Deficient afo1- Yeast Suppressed by a Dominant Mutation in ATP3

A yeast deletion mutation in the nuclear-encoded gene, AFO1, which codes for a mitochondrial ribosomal protein, led to slow growth on glucose, the inability to grow on glycerol or ethanol, and loss of mitochondrial DNA and respiration. We noticed that afo1- yeast readily obtains secondary mutations that suppress aspects of this phenotype, including its growth defect. We characterized and identified a dominant missense suppressor mutation in the ATP3 gene. Comparing isogenic slowly growing rho-zero and rapidly growing suppressed afo1- strains under carefully controlled fermentation conditions showed that energy charge was not significantly different between strains and was not causal for the observed growth properties. Surprisingly, in a wild-type background, the dominant suppressor allele of ATP3 still allowed respiratory growth but increased the petite frequency. Similarly, a slow-growing respiratory deficient afo1- strain displayed an about twofold increase in spontaneous frequency of point mutations (comparable to the rho-zero strain) while the suppressed strain showed mutation frequency comparable to the respiratory-competent WT strain. We conclude, that phenotypes that result from afo1- are mostly explained by rapidly emerging mutations that compensate for the slow growth that typically follows respiratory deficiency.

Induction of ER Stress by an AAV5 BDD FVIII Construct Is Dependent on the Strength of the Hepatic-Specific Promoter

Adeno-associated virus 5 (AAV5)-human factor VIII-SQ (hFVIII-SQ; valoctocogene roxaparvovec) is an AAV-mediated product under evaluation for treatment of severe hemophilia A, which contains a B-domain-deleted hFVIII (hFVIII-SQ) transgene and a hybrid liver-specific promotor (HLP). To increase FVIII-SQ expression and reduce the vector dose required, a stronger promoter may be considered. However, because FVIII-SQ is a protein known to be difficult to fold and secrete, this could potentially induce endoplasmic reticulum (ER) stress. We evaluated the effect of two AAV5-hFVIII-SQ vectors with different liver-specific promoter strength (HLP << 100ATGB) on hepatic ER stress in mice. Five weeks after receiving vehicle or vector, the percentage of transduced hepatocytes and levels of liver hFVIII-SQ DNA and RNA increased dose dependently for both vectors. At lower doses, plasma hFVIII-SQ protein levels were higher for 100ATGB. This difference was attenuated at the highest dose. For 100ATGB, liver hFVIII-SQ protein accumulated dose dependently, with increased expression of ER stress markers at the highest dose, suggesting hepatocytes reached or exceeded their capacity to fold/secrete hFVIII-SQ. These data suggest that weaker promoters may require relatively higher doses to distribute expression load across a greater number of hepatocytes, whereas relatively stronger promoters may produce comparable levels of FVIII in fewer hepatocytes, with potential for ER stress.

Abstract 4266: Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition

Precision oncology requires a deep understanding of the molecular mechanism in cancer biology. The predominant approach today focuses on genome structure and gene expression data, which have become available with the rise of next-gen sequencing technology. On a phenotypic level, however, protein expression and activation are arguably more directly related to cellular function. The availability of combined genome and proteome level data from the same tumor is therefore expected to provide a much more complete picture of a tumor in a particular state. Until recently, proteomics technology could not match the scale of next-gen sequencing and consequently, precision medicine research has almost exclusively been based on gene-level data. Here we show the first truly large-scale data set for protein expression and protein phosphorylation for a large collection of biospecimens derived from the IndivuType cohort of Indivumed, Germany. Matching fresh-frozen tumor and adjacent normal tissue samples from thousands of patients including various cancer entities were obtained from Indivumed's network of partner hospitals Enabled by the novel data-independent acquisition (DIA) workflow, a mass spectrometric method that obtains peptide fragmentation data in a highly parallelized way with high reproducibility and sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were analyzed. Sample processing from 5 mg of tissue per sample was performed on 96-well plates with the help of a liquid handling robot. Phospho-peptide enrichment was carried out using a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads (ReSyn Biosciences). Data-independent acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. With a throughput of 850 WP and 650 PP samples per month, thousands of samples were analyzed to date. The resulting proteome data is integrated into Indivumed's IndivuType multi-omics database, supporting the identification and validation of new molecular cancer drug targets and biomarkers.

Citation Format: Jakob Vowinckel, Karel Novy, Thomas Corwin, Tobias Treiber, Roland Bruderer, Lukas Reiter, Eike von Leitner, Oliver Rinner, Claudia Escher. Proteomics for precision oncology: Profiling the proteome of matching tumor and adjacent normal tissue using data-independent acquisition [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 4266.

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

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.

Abstract 5529: Proteomic profiling of FFPE tumors samples from melanoma subjects treated with anti-PD-1 immunotherapy identifies proteins associated with response to treatment

Background Immune checkpoint inhibitors (ICI) have greatly improved the treatment options for patients with advanced stage melanoma, with improved clinical responses and overall survival compared to standard systemic therapies. However, a large percentage of melanoma patients do not respond to ICIs, highlighting the need for a greater understanding of the tumor environment and host immune response. Here, we apply unbiased discovery proteomics, based on label-free data independent acquisition (DIA) mass spectrometry, to deeply characterize global tumor proteomes to identify proteins and pathways that are associated with pre-treatment response to anti-PD-1 immunotherapy.

Methods Unbiased, data-independent acquisition (DIA) mass spectrometry was used to analyze formalin fixed paraffin imbedded (FFPE) tumor tissue samples from subjects with Stage III-IV melanoma which were resected prior to initiation of first-line anti-PD-1 ICI therapy. The selected samples represent two distinct clinical subgroups; those who received clinical benefit, with a stable disease or better (SD, PR and CR, n = 13), and those with no clinical benefit (PD, n = 9). Samples were prepared for mass spectrometry using standard procedures. All samples were analyzed using 4-hour gradients on a LC-MS/MS setup operated in DIA mode. Data was extracted using Spectronaut (Biognosys) with a sample specific spectral library which was combined with a large human tissue resource library. Statistical analysis was conducted to identify proteins that are up- or down-regulated with respect to benefit group. Pathway analysis was also conducted to highlight dysregulated biological functions and pathways.

Results In analysis with 2-hour gradients, &gt;7,500 proteins were quantified across all samples. Univariate statistical testing between groups identified 254 proteins are dysregulated (120 up- and 134 down-regulated) in subjects who received clinical benefit, of which a subset of 25 proteins was identified that describe the variance between the two sample groups. When annotated to their sub-cellular location, all up-regulated species are identified as mitochondrial proteins, indicating an enhanced metabolic environment in the responder subgroup. Additionally, GM2A and PLEKHA5 were strong diagnostic predictors of responder status. This updated analysis, conducted with a deeper level of characterization, will focus on additional metabolic pathways as well as known proteins associated with metabolic resistance (e.g. CD39 and CD73) to more fully characterize the tumor metabolic environment.

Conclusions Global profiling of the tumor proteome provides a unique characterization of melanoma tumor biology. A pathway level analysis shows increased metabolic processes may underly some of the differences in benefit related to ICI therapy.

Citation Format: Nicholas Dupuis, Jakob Vowinckel, Domenico Mallardo, Mariaelena Capone, Madonna Gabriele, Antonio Sorrentino, Vito Vanella, Daniel Heinzmann, Paolo Ascierto. Proteomic profiling of FFPE tumors samples from melanoma subjects treated with anti-PD-1 immunotherapy identifies proteins associated with response to 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 5529.

Next generation proteomics in precision oncology: 1000s of proteome and phosphoproteome profiles of tumors and matching healthy tissues as meaningful layer in multi-omics database

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Background: The rise of precision oncology therapeutics requires deep understanding of all molecular mechanisms involved in cancer biology. IndivuType offers the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics (WGS), transcriptomics, proteomics, and clinical outcome information. Indivumed is committed to the quality of the IndivuType ecosystem starting with stringent SOP-driven sample collection combined with thorough validation of clinical information and data integrity. The availability of multi-omics data from the same tumor can provide a comprehensive molecular picture of cancer for a given patient. Protein expression and activation are directly related to cellular function and hence provide actionable information about druggable targets. Until recently, the proteomics technology could not match the scale of next-gen sequencing and consequently precision medicine has almost exclusively been based on gene level data. Here we present the first large-scale data set for protein expression and phosphorylation. Enabled by the data independent acquisition (DIA) workflow, a mass spectrometric method provided by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome (WP) and 20,000 phospho-peptides in the phospho-proteome (PP) workflow were profiled. Methods: Sample processing from 5 mg of tissue per sample was performed using liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. DIA LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (WP) and 60 min (PP) gradients. Results: Several thousands of high-quality patient samples of various cancer types have been analyzed to date. The resulting proteome and phospho-proteome data has been integrated into the IndivuType database, thereby providing a solid foundation to advance our understanding of cancer. Conclusions: With the ongoing addition of more samples and associated deep and rich data, the platform could unravel key molecular events and is expected to transform knowledge into actionable treatments and personalized therapies.

Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism

Both single and multicellular organisms depend on anti-stress mechanisms that enable them to deal with sudden changes in the environment, including exposure to heat and oxidants. Central to the stress response are dynamic changes in metabolism, such as the transition from the glycolysis to the pentose phosphate pathway-a conserved first-line response to oxidative insults1,2. Here we report a second metabolic adaptation that protects microbial cells in stress situations. The role of the yeast polyamine transporter Tpo1p3-5 in maintaining oxidant resistance is unknown6. However, a proteomic time-course experiment suggests a link to lysine metabolism. We reveal a connection between polyamine and lysine metabolism during stress situations, in the form of a promiscuous enzymatic reaction in which the first enzyme of the polyamine pathway, Spe1p, decarboxylates lysine and forms an alternative polyamine, cadaverine. The reaction proceeds in the presence of extracellular lysine, which is taken up by cells to reach concentrations up to one hundred times higher than those required for growth. Such extensive harvest is not observed for the other amino acids, is dependent on the polyamine pathway and triggers a reprogramming of redox metabolism. As a result, NADPH-which would otherwise be required for lysine biosynthesis-is channelled into glutathione metabolism, leading to a large increase in glutathione concentrations, lower levels of reactive oxygen species and increased oxidant tolerance. Our results show that nutrient uptake occurs not only to enable cell growth, but when the nutrient availability is favourable it also enables cells to reconfigure their metabolism to preventatively mount stress protection.

Abstract 4547: High dimensional proteomic profiling of immune cell subsets with data-independent acquisition mass spectrometry

Background Recent success with therapies to activate the immune system has demonstrated the utility of targeting the immune system for control of multiple cancers. These successes have also spurred interest in characterizing immune cell sub-populations to understand mechanisms of activation and suppression, and their relationship to therapeutic response. Currently, antibody-based approaches are commonly used to characterize immune cells, however these methods are limited to 30-40 markers and are driven by previous hypotheses, limiting new discovery. In this context, determination of the surface and cellular proteome of responsive populations will provide a powerful tool for insight into response mechanisms, so far hampered by low sample material availability and insufficient sensitivity of proteomic methodology. Here, we demonstrate how data-independent acquisition mass spectrometry can be used for high-dimensional characterization of immune cells, even with very limited cell numbers. Methods Primary human Cytotoxic CD8+ T cells, CD4+ T cells, CD14+ monocytes and natural killer (NK) cells, isolated from peripheral blood mononuclear cells, were prepared for mass spectrometry using standard sample preparation workflows. All samples were analyzed using 2 and 4 hour LC gradients on a C18 column coupled to a Thermo Scientific Q Exactive HF mass spectrometer in data-independent acquisition (DIA-MS) mode. DIA data was extracted using Spectronaut Pulsar X (Biognosys) both with a library generated using directDIA data searching in Spectromine as well as a Hybrid Library combining the directDIA and a publicly available resource library. Results Cytotoxic CD8+ T cells were evaluated using 100,000 cells of input material, which resulted in more than 3,500 proteins quantified in the primary cells. When combined with a CD8+ T cell resource library, the number of proteins quantified was more than 5,000. In the current experimental setup, 30,000 cells defines the lower limit of detection of CD8A and CD8B. Among other previously characterized proteins associated with CD8+ T cells, CCL5, TBX21, GZMH, PRF1, GNLY, CST7 were all detected at 30,000 cell input. Additionally, Granzyme A and B were also quantified which have classically been used, along with PRF1, as markers of lymphocyte infiltration. Data will also be presented for CD4+ T cells, CD14+ monocytes, and NK cells to further map and compare the immune cell phenotypic landscapes. Conclusions The DIA-MS platform enables deep proteomic phenotyping of sorted immune cell samples, even with limited numbers of cells. These new data sets make available broad and un-constrained biomarker investigation for deconvolution of the processes driving immune cell activation and suppression.

Citation Format: Jakob Vowinckel, Tobias Treiber, Kristina Beeler, Nicholas Dupuis. High dimensional proteomic profiling of immune cell subsets with data-independent acquisition mass spectrometry [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 4547.

Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity

Some species responded successfully to prehistoric changes in climate [1, 2], while others failed to adapt and became extinct [3]. The factors that determine successful climate adaptation remain poorly understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the "ice-age" climate of the Pleistocene steppe [4, 5]. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow [6, 7]. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot's adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

Variable repeats in the eukaryotic polyubiquitin gene ubi4 modulate proteostasis and stress survival

Ubiquitin conjugation signals for selective protein degradation by the proteasome. In eukaryotes, ubiquitin is encoded both as a monomeric ubiquitin unit fused to a ribosomal gene and as multiple ubiquitin units in tandem. The polyubiquitin gene is a unique, highly conserved open reading frame composed solely of tandem repeats, yet it is still unclear why cells utilize this unusual gene structure. Using the Saccharomyces cerevisiae UBI4 gene, we show that this multi-unit structure allows cells to rapidly produce large amounts of ubiquitin needed to respond to sudden stress. The number of ubiquitin units encoded by UBI4 influences cellular survival and the rate of ubiquitin-proteasome system (UPS)-mediated proteolysis following heat stress. Interestingly, the optimal number of repeats varies under different types of stress indicating that natural variation in repeat numbers may optimize the chance for survival. Our results demonstrate how a variable polycistronic transcript provides an evolutionary alternative for gene copy number variation.

Eukaryotic cells rely on the ubiquitin-proteasome system for selective degradation of proteins, a process vital to organismal fitness. Here the authors show that the number of repeats in the polyubiquitin gene is evolutionarily unstable within and between yeast species, and that this variability may tune the cell’s capacity to respond to sudden environmental perturbations.

Analysis of Drosophila melanogaster proteome dynamics during embryonic development by a combination of label-free proteomics approaches

During embryogenesis, organisms undergo considerable cellular remodelling requiring the combined action of thousands of proteins. In case of the well-studied model Drosophila melanogaster, transcriptomic studies, most notably from the modENCODE project, have described in detail changes in gene expression at the mRNA level across development. Although such data are clearly very useful to understand how the genome is regulated during embryogenesis, it is important to understand how changes in gene expression are reflected at the level of the proteome. In this study, we describe a combination of two quantitative label-free approaches, SWATH and data-dependent acquisition, to monitor changes in protein expression across a timecourse of D. melanogaster embryonic development. We demonstrate that both approaches provide robust and reproducible methods for the analysis of proteome changes. In a preliminary analysis of Drosophila embryogenesis, we identified several pathways, including the heat-shock response, nuclear protein import and energy production that are regulated during embryo development. In some cases changes in protein expression mirrored transcript levels across development, whereas other proteins showed signatures of post-transcriptional regulation. Taken together, our pilot study provides a solid platform for a more detailed exploration of the embryonic proteome.

Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community

Cells that grow together respond heterogeneously to stress even when they are genetically similar. Metabolism, a key determinant of cellular stress tolerance, may be one source of this phenotypic heterogeneity, however, this relationship is largely unclear. We used self‐establishing metabolically cooperating (SeMeCo) yeast communities, in which metabolic cooperation can be followed on the basis of genotype, as a model to dissect the role of metabolic cooperation in single‐cell heterogeneity. Cells within SeMeCo communities showed to be highly heterogeneous in their stress tolerance, while the survival of each cell under heat or oxidative stress, was strongly determined by its metabolic specialization. This heterogeneity emerged for all metabolite exchange interactions studied (histidine, leucine, uracil, and methionine) as well as oxidant (H₂O₂, diamide) and heat stress treatments. In contrast, the SeMeCo community collectively showed to be similarly tolerant to stress as wild‐type populations. Moreover, stress heterogeneity did not establish as sole consequence of metabolic genotype (auxotrophic background) of the single cell, but was observed only for cells that cooperated according to their metabolic capacity. We therefore conclude that phenotypic heterogeneity and cell to cell differences in stress tolerance are emergent properties when cells cooperate in metabolism.

Methionine Metabolism Alters Oxidative Stress Resistance via the Pentose Phosphate Pathway

Nutrient uptake and metabolism have a significant impact on the way cells respond to stress. The amino acid methionine is, in particular, a key player in the oxidative stress response, and acting as a reactive oxygen species scavenger, methionine is implicated in caloric restriction phenotypes and aging. We here provide evidence that some effects of methionine in stress situations are indirect and caused by altered activity of the nicotinamide adenine dinucleotide phosphate (NADPH) producing oxidative part of the pentose phosphate pathway (PPP). In Saccharomyces cerevisiae, both methionine prototrophic (MET15) and auxotrophic (met15Δ) cells supplemented with methionine showed an increase in PPP metabolite concentrations downstream of the NADPH producing enzyme, 6-phosphogluconate dehydrogenase. Proteomics revealed this enzyme to also increase in expression compared to methionine self-synthesizing cells. Oxidant tolerance was increased in cells preincubated with methionine; however, this effect was abolished when flux through the oxidative PPP was prevented by deletion of its rate limiting enzyme, ZWF1. Stress resistance phenotypes that follow methionine supplementation hence involve the oxidative PPP. Effects of methionine on oxidative metabolism, stress signaling, and aging have thus to be seen in the context of an altered activity of this NADP reducing pathway.

Self-establishing communities enable cooperative metabolite exchange in a eukaryote

Metabolite exchange among co-growing cells is frequent by nature, however, is not necessarily occurring at growth-relevant quantities indicative of non-cell-autonomous metabolic function. Complementary auxotrophs of Saccharomyces cerevisiae amino acid and nucleotide metabolism regularly fail to compensate for each other's deficiencies upon co-culturing, a situation which implied the absence of growth-relevant metabolite exchange interactions. Contrastingly, we find that yeast colonies maintain a rich exometabolome and that cells prefer the uptake of extracellular metabolites over self-synthesis, indicators of ongoing metabolite exchange. We conceived a system that circumvents co-culturing and begins with a self-supporting cell that grows autonomously into a heterogeneous community, only able to survive by exchanging histidine, leucine, uracil, and methionine. Compensating for the progressive loss of prototrophy, self-establishing communities successfully obtained an auxotrophic composition in a nutrition-dependent manner, maintaining a wild-type like exometabolome, growth parameters, and cell viability. Yeast, as a eukaryotic model, thus possesses extensive capacity for growth-relevant metabolite exchange and readily cooperates in metabolism within progressively establishing communities.

Using the canary genome to decipher the evolution of hormone-sensitive gene regulation in seasonal singing birds

BACKGROUND

While the song of all songbirds is controlled by the same neural circuit, the hormone dependence of singing behavior varies greatly between species. For this reason, songbirds are ideal organisms to study ultimate and proximate mechanisms of hormone-dependent behavior and neuronal plasticity.

RESULTS

We present the high quality assembly and annotation of a female 1.2-Gbp canary genome. Whole genome alignments between the canary and 13 genomes throughout the bird taxa show a much-conserved synteny, whereas at the single-base resolution there are considerable species differences. These differences impact small sequence motifs like transcription factor binding sites such as estrogen response elements and androgen response elements. To relate these species-specific response elements to the hormone-sensitivity of the canary singing behavior, we identify seasonal testosterone-sensitive transcriptomes of major song-related brain regions, HVC and RA, and find the seasonal gene networks related to neuronal differentiation only in the HVC. Testosterone-sensitive up-regulated gene networks of HVC of singing males concerned neuronal differentiation. Among the testosterone-regulated genes of canary HVC, 20% lack estrogen response elements and 4 to 8% lack androgen response elements in orthologous promoters in the zebra finch.

CONCLUSIONS

The canary genome sequence and complementary expression analysis reveal intra-regional evolutionary changes in a multi-regional neural circuit controlling seasonal singing behavior and identify gene evolution related to the hormone-sensitivity of this seasonal singing behavior. Such genes that are testosterone- and estrogen-sensitive specifically in the canary and that are involved in rewiring of neurons might be crucial for seasonal re-differentiation of HVC underlying seasonal song patterning.

Mitochondria in ageing: there is metabolism beyond the ROS

Mitochondria are responsible for a series of metabolic functions. Superoxide leakage from the respiratory chain and the resulting cascade of reactive oxygen species-induced damage, as well as mitochondrial metabolism in programmed cell death, have been intensively studied during ageing in single-cellular and higher organisms. Changes in mitochondrial physiology and metabolism resulting in ROS are thus considered to be hallmarks of ageing. In this review, we address 'other' metabolic activities of mitochondria, carbon metabolism (the TCA cycle and related underground metabolism), the synthesis of Fe/S clusters and the metabolic consequences of mitophagy. These important mitochondrial activities are hitherto less well-studied in the context of cellular and organismic ageing. In budding yeast, they strongly influence replicative, chronological and hibernating lifespan, connecting the diverse ageing phenotypes studied in this single-cellular model organism. Moreover, there is evidence that similar processes equally contribute to ageing of higher organisms as well. In this scenario, increasing loss of metabolic integrity would be one driving force that contributes to the ageing process. Understanding mitochondrial metabolism may thus be required for achieving a unifying theory of eukaryotic ageing.

The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics

The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides.

The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics

The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides.

Histaminylation of glutamine residues is a novel posttranslational modification implicated in G-protein signaling

Posttranslational modifications (PTM) have been shown to be essential for protein function and signaling. Here we report the identification of a novel modification, protein transfer of histamine, and provide evidence for its function in G protein signaling. Histamine, known as neurotransmitter and mediator of the inflammatory response, was found incorporated into mastocytoma proteins. Histaminylation was dependent on transglutaminase II. Mass spectrometry confirmed histamine modification of the small and heterotrimeric G proteins Cdc42, Gαo1 and Gαq. The modification was specific for glutamine residues in the catalytic core, and triggered their constitutive activation. TGM2-mediated histaminylation is thus a novel PTM that functions in G protein signaling. Protein αmonoaminylations, thus including histaminylation, serotonylation, dopaminylation and norepinephrinylation, hence emerge as a novel class of regulatory PTMs.

Intracellular serotonin modulates insulin secretion from pancreatic beta-cells by protein serotonylation

Non-neuronal, peripheral serotonin deficiency causes diabetes mellitus and identifies an intracellular role for serotonin in the regulation of insulin secretion.

While serotonin (5-HT) co-localization with insulin in granules of pancreatic β-cells was demonstrated more than three decades ago, its physiological role in the etiology of diabetes is still unclear. We combined biochemical and electrophysiological analyses of mice selectively deficient in peripheral tryptophan hydroxylase (Tph1−/−) and 5-HT to show that intracellular 5-HT regulates insulin secretion. We found that these mice are diabetic and have an impaired insulin secretion due to the lack of 5-HT in the pancreas. The pharmacological restoration of peripheral 5-HT levels rescued the impaired insulin secretion in vivo. These findings were further evidenced by patch clamp experiments with isolated Tph1−/− β-cells, which clearly showed that the secretory defect is downstream of Ca²⁺-signaling and can be rescued by direct intracellular application of 5-HT via the clamp pipette. In elucidating the underlying mechanism further, we demonstrate the covalent coupling of 5-HT by transglutaminases during insulin exocytosis to two key players in insulin secretion, the small GTPases Rab3a and Rab27a. This renders them constitutively active in a receptor-independent signaling mechanism we have recently termed serotonylation. Concordantly, an inhibition of such activating serotonylation in β-cells abates insulin secretion. We also observed inactivation of serotonylated Rab3a by enhanced proteasomal degradation, which is in line with the inactivation of other serotonylated GTPases. Our results demonstrate that 5-HT regulates insulin secretion by serotonylation of GTPases within pancreatic β-cells and suggest that intracellular 5-HT functions in various microenvironments via this mechanism in concert with the known receptor-mediated signaling.

Diabetes is the most prevalent metabolic disease and one that affects individuals of every social and economic status. The disease can arise as a result of reduced secretion of insulin from pancreatic β-cells or reduced action of insulin on its target organs. Therefore, understanding how to prevent and treat diabetes requires an extensive knowledge of the regulation of insulin secretion. In this study, we identify the hormone serotonin as a new regulator of insulin secretion and thereby attribute a function to the co-localization of serotonin and insulin in pancreatic β-cells that was first observed 30 years ago but until now not understood. We first demonstrate that a lack of serotonin in β-cells of transgenic mice leads to reduced insulin secretion and diabetes mellitus and that pharmacological replenishment of serotonin rescues insulin secretion in these mice. Interestingly, serotonin mainly acts not as an intercellular signaling molecule via its traditional surface receptors but intracellularly via regulation of the activity of target proteins through covalent coupling of serotonin to them. This coupling, called serotonylation, activates specific small GTPases, which in turn promote glucose-mediated insulin secretion. Adding this receptor-independent signaling mechanism to the multifarious regulatory functions of serotonin, we hypothesize that protein serotonylation modulates physiological secretion processes in all serotonin-containing tissues.

A mammalianized synthetic nitroreductase gene for high-level expression

Background

The nitroreductase/5-(azaridin-1-yl)-2,4-dinitrobenzamide (NTR/CB1954) enzyme/prodrug system is considered as a promising candidate for anti-cancer strategies by gene-directed enzyme prodrug therapy (GDEPT) and has recently entered clinical trials. It requires the genetic modification of tumor cells to express the E. coli enzyme nitroreductase that bioactivates the prodrug CB1954 to a powerful cytotoxin. This metabolite causes apoptotic cell death by DNA interstrand crosslinking. Enhancing the enzymatic NTR activity for CB1954 should improve the therapeutical potential of this enzyme-prodrug combination in cancer gene therapy.

Methods

We performed de novo synthesis of the bacterial nitroreductase gene adapting codon usage to mammalian preferences. The synthetic gene was investigated for its expression efficacy and ability to sensitize mammalian cells to CB1954 using western blotting analysis and cytotoxicity assays.

Results

In our study, we detected cytoplasmic protein aggregates by expressing GFP-tagged NTR in COS-7 cells, suggesting an impaired translation by divergent codon usage between prokaryotes and eukaryotes. Therefore, we generated a synthetic variant of the nitroreductase gene, called ntro, adapted for high-level expression in mammalian cells. A total of 144 silent base substitutions were made within the bacterial ntr gene to change its codon usage to mammalian preferences. The codon-optimized ntro either tagged to gfp or c-myc showed higher expression levels in mammalian cell lines. Furthermore, the ntro rendered several cell lines ten times more sensitive to the prodrug CB1954 and also resulted in an improved bystander effect.

Conclusion

Our results show that codon optimization overcomes expression limitations of the bacterial ntr gene in mammalian cells, thereby improving the NTR/CB1954 system at translational level for cancer gene therapy in humans.

Serotonylation of small GTPases is a signal transduction pathway that triggers platelet alpha-granule release

Serotonin is a neurotransmitter in the central nervous system. In the periphery, serotonin functions as a ubiquitous hormone involved in vasoconstriction and platelet function. Serotonin is synthesized independently in peripheral tissues and neurons by two different rate-limiting tryptophan hydroxylase (TPH) isoenzymes. Here, we show that mice selectively deficient in peripheral TPH and serotonin exhibit impaired hemostasis, resulting in a reduced risk of thrombosis and thromboembolism, although the ultrastructure of the platelets is not affected. While the aggregation of serotonin-deficient platelets in vitro is apparently normal, their adhesion in vivo is reduced due to a blunted secretion of adhesive alpha-granular proteins. In elucidating the mechanism further, we demonstrate that serotonin is transamidated to small GTPases by transglutaminases during activation and aggregation of platelets, rendering these GTPases constitutively active. Our data provides evidence for a receptor-independent signaling mechanism, termed herein as "serotonylation," which leads to alpha-granule exocytosis from platelets.