SOX11 is a novel binding partner and endogenous inhibitor of SAMHD1 ara-CTPase activity in mantle cell lymphoma.

The sterile alpha motif and histidine-aspartate (HD) domain containing protein 1 (SAMHD1) is a deoxynucleoside triphosphate triphosphohydrolase with ara-CTPase activity that confers cytarabine (ara-C) resistance in several haematological malignancies. Targeting SAMHD1's ara-CTPase activity has recently been demonstrated to enhance ara-C efficacy in acute myeloid leukemia. Here, we identify the transcription factor SRY-related HMG-box containing protein 11 (SOX11) as a novel direct binding partner and first known endogenous inhibitor of SAMHD1. SOX11 is aberrantly expressed not only in mantle cell lymphoma (MCL), but also in some Burkitt lymphomas. Co-immunoprecipitation of SOX11 followed by mass spectrometry in MCL cell lines identified SAMHD1 as the top SOX11 interaction partner which was validated by proximity ligation assay. In vitro, SAMHD1 bound to the HMG box of SOX11 with low-micromolar affinity. In situ crosslinking studies further indicated that SOX11-SAMHD1 binding resulted in a reduced tetramerization of SAMHD1. Functionally, expression of SOX11 inhibited SAMHD1 ara-CTPase activity in a dose-dependent manner resulting in ara-C sensitization in cell lines and in a SOX11-inducible mouse model of MCL. In SOX11-negative MCL, SOX11-mediated ara-CTPase inhibition could be mimicked by adding the recently identified SAMHD1 inhibitor hydroxyurea. Taken together, our results identify SOX11 as a novel SAMHD1 interaction partner and its first known endogenous inhibitor with potentially important implications for clinical therapy stratification.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer.

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

SOX11 is a novel binding partner and endogenous inhibitor of SAMHD1 ara-CTPase activity in mantle cell lymphoma.

ABSTRACT: Sterile alpha motif and histidine-aspartate (HD) domain-containing protein 1 (SAMHD1) is a deoxynucleoside triphosphate triphosphohydrolase with ara-CTPase activity that confers cytarabine (ara-C) resistance in several hematological malignancies. Targeting SAMHD1's ara-CTPase activity has recently been demonstrated to enhance ara-C efficacy in acute myeloid leukemia. Here, we identify the transcription factor SRY-related HMG-box containing protein 11 (SOX11) as a novel direct binding partner and first known endogenous inhibitor of SAMHD1. SOX11 is aberrantly expressed not only in mantle cell lymphoma (MCL), but also in some Burkitt lymphomas. Coimmunoprecipitation of SOX11 followed by mass spectrometry in MCL cell lines identified SAMHD1 as the top SOX11 interaction partner, which was validated by proximity ligation assay. In vitro, SAMHD1 bound to the HMG box of SOX11 with low-micromolar affinity. In situ crosslinking studies further indicated that SOX11-SAMHD1 binding resulted in a reduced tetramerization of SAMHD1. Functionally, expression of SOX11 inhibited SAMHD1 ara-CTPase activity in a dose-dependent manner resulting in ara-C sensitization in cell lines and in a SOX11-inducible mouse model of MCL. In SOX11-negative MCL, SOX11-mediated ara-CTPase inhibition could be mimicked by adding the recently identified SAMHD1 inhibitor hydroxyurea. Taken together, our results identify SOX11 as a novel SAMHD1 interaction partner and its first known endogenous inhibitor with potentially important implications for clinical therapy stratification.

FBXL12 degrades FANCD2 to regulate replication recovery and promote cancer cell survival under conditions of replication stress.

Fanconi anemia (FA) signaling, a key genomic maintenance pathway, is activated in response to replication stress. Here, we report that phosphorylation of the pivotal pathway protein FANCD2 by CHK1 triggers its FBXL12-dependent proteasomal degradation, facilitating FANCD2 clearance at stalled replication forks. This promotes efficient DNA replication under conditions of CYCLIN E- and drug-induced replication stress. Reconstituting FANCD2-deficient fibroblasts with phosphodegron mutants failed to re-establish fork progression. In the absence of FBXL12, FANCD2 becomes trapped on chromatin, leading to replication stress and excessive DNA damage. In human cancers, FBXL12, CYCLIN E, and FA signaling are positively correlated, and FBXL12 upregulation is linked to reduced survival in patients with high CYCLIN E-expressing breast tumors. Finally, depletion of FBXL12 exacerbated oncogene-induced replication stress and sensitized cancer cells to drug-induced replication stress by WEE1 inhibition. Collectively, our results indicate that FBXL12 constitutes a vulnerability and a potential therapeutic target in CYCLIN E-overexpressing cancers.

Proteome Analysis of Pancreatic Tumors Implicates Extracellular Matrix in Patient Outcome.

Pancreatic cancer remains a disease with unmet clinical needs and inadequate diagnostic, prognostic, and predictive biomarkers. In-depth characterization of the disease proteome is limited. This study thus aims to define and describe protein networks underlying pancreatic cancer and identify protein centric subtypes with clinical relevance. Mass spectrometry-based proteomics was used to identify and quantify the proteome in tumor tissue, tumor-adjacent tissue, and patient-derived xenografts (PDX)-derived cell lines from patients with pancreatic cancer, and tissues from patients with chronic pancreatitis. We identified, quantified, and characterized 11,634 proteins from 72 pancreatic tissue samples. Network focused analysis of the proteomics data led to identification of a tumor epithelium-specific module and an extracellular matrix (ECM)-associated module that discriminated pancreatic tumor tissue from both tumor adjacent tissue and pancreatitis tissue. On the basis of the ECM module, we defined an ECM-high and an ECM-low subgroup, where the ECM-high subgroup was associated with poor prognosis (median survival months: 15.3 vs. 22.9 months; log-rank test, P = 0.02). The ECM-high tumors were characterized by elevated epithelial-mesenchymal transition and glycolytic activities, and low oxidative phosphorylation, E2F, and DNA repair pathway activities. This study offers novel insights into the protein network underlying pancreatic cancer opening up for proteome precision medicine development. Significance: Pancreatic cancer lacks reliable biomarkers for prognostication and treatment of patients. We analyzed the proteome of pancreatic tumors, nonmalignant tissues of the pancreas and PDX-derived cell lines, and identified proteins that discriminate between patients with good and poor survival. The proteomics data also unraveled potential novel drug targets.

Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms.

Despite major advancements in lung cancer treatment, long-term survival is still rare, and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune evasion mechanisms. Here we performed in-depth mass spectrometry (MS)-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and subtype-specific expression of immune checkpoints. Unexpectedly, high neoantigen burden was linked to global hypomethylation and complex neoantigens mapped to genomic regions, such as endogenous retroviral elements and introns, in immune-cold subtypes. Further, we linked immune evasion with LAG3 via STK11 mutation-dependent HNF1A activation and FGL1 expression. Finally, we develop a data-independent acquisition MS-based NSCLC subtype classification method, validate it in an independent cohort of 208 NSCLC cases and demonstrate its clinical utility by analyzing an additional cohort of 84 late-stage NSCLC biopsy samples.

Reprogrammed transsulfuration promotes basal-like breast tumor progression via realigning cellular cysteine persulfidation.

Basal-like breast cancer (BLBC) is the most aggressive subtype of breast tumors with poor prognosis and limited molecular-targeted therapy options. We show that BLBC cells have a high Cys demand and reprogrammed Cys metabolism. Patient-derived BLBC tumors from four different cohorts exhibited elevated expression of the transsulfuration enzyme cystathione ß-synthetase (CBS). CBS silencing (shCBS) made BLBC cells less invasive, proliferate slower, more vulnerable to oxidative stress and cystine (CySSCy) deprivation, prone to ferroptosis, and less responsive to HIF1-α activation under hypoxia. shCBS xenograft tumors grew slower than controls and exhibited impaired angiogenesis and larger necrotic areas. Sulfur metabolite profiling suggested that realigned sulfide/persulfide-inducing functions of CBS are important in BLBC tumor progression. Supporting this, the exclusion of serine, a substrate of CBS for producing Cys but not for producing sulfide/persulfide, did not exacerbate CySSCy deprivation-induced ferroptosis in shCBS BLBC cells. Impaired Tyr phosphorylation was detected in shCBS cells and xenografts, likely due to persulfidation-inhibited phosphatase functions. Overexpression of cystathione γ-lyase (CSE), which can also contribute to cellular sulfide/persulfide production, compensated for the loss of CBS activities, and treatment of shCBS xenografts with a CSE inhibitor further blocked tumor growth. Glutathione and protein-Cys levels were not diminished in shCBS cells or xenografts, but levels of Cys persulfidation and the persulfide-catabolizing enzyme ETHE1 were suppressed. Finally, expression of enzymes of the oxidizing Cys catabolism pathway was diminished, but expression of the persulfide-producing CARS2 was elevated in human BLBC tumors. Hence, the persulfide-producing pathways are major targetable determinants of BLBC pathology that could be therapeutically exploited.

Correcting for Naturally Occurring Mass Isotopologue Abundances in Stable-Isotope Tracing Experiments with PolyMID.

Stable-isotope tracing is a method to measure intracellular metabolic pathway utilization by feeding a cellular system a stable-isotope-labeled tracer nutrient. The power of the method to resolve differential pathway utilization is derived from the enrichment of metabolites in heavy isotopes that are synthesized from the tracer nutrient. However, the readout is complicated by the presence of naturally occurring heavy isotopes that are not derived from the tracer nutrient. Herein we present an algorithm, and a tool that applies it (PolyMID-Correct, part of the PolyMID software package), to computationally remove the influence of naturally occurring heavy isotopes. The algorithm is applicable to stable-isotope tracing data collected on low- and high- mass resolution mass spectrometers. PolyMID-Correct is open source and available under an MIT license.

Mutation-independent Proteomic Signatures of Pathological Progression in Murine Models of Duchenne Muscular Dystrophy.

The absence of the dystrophin protein in Duchenne muscular dystrophy (DMD) results in myofiber fragility and a plethora of downstream secondary pathologies. Although a variety of experimental therapies are in development, achieving effective treatments for DMD remains exceptionally challenging, not least because the pathological consequences of dystrophin loss are incompletely understood. Here we have performed proteome profiling in tibialis anterior muscles from two murine DMD models (mdx and mdx52) at three ages (8, 16, and 80 weeks of age), all n = 3. High-resolution isoelectric focusing liquid chromatography-tandem MS (HiRIEF-LC-MS/MS) was used to quantify the expression of 4974 proteins across all 27 samples. The two dystrophic models were found to be highly similar, whereas multiple proteins were differentially expressed relative to WT (C57BL/6) controls at each age. Furthermore, 1795 proteins were differentially expressed when samples were pooled across ages and dystrophic strains. These included numerous proteins associated with the extracellular matrix and muscle function that have not been reported previously. Pathway analysis revealed multiple perturbed pathways and predicted upstream regulators, which together are indicative of cross-talk between inflammatory, metabolic, and muscle growth pathways (e.g. TNF, INFγ, NF-κB, SIRT1, AMPK, PGC-1α, PPARs, ILK, and AKT/PI3K). Upregulation of CAV3, MVP and PAK1 protein expression was validated in dystrophic muscle by Western blot. Furthermore, MVP was upregulated during, but not required for, the differentiation of C2C12 myoblasts suggesting that this protein may affect muscle regeneration. This study provides novel insights into mutation-independent proteomic signatures characteristic of the dystrophic phenotype and its progression with aging.

Immediate Adaptation Analysis Implicates BCL6 as an EGFR-TKI Combination Therapy Target in NSCLC.

Drug resistance is a major obstacle to curative cancer therapies, and increased understanding of the molecular events contributing to resistance would enable better prediction of therapy response, as well as contribute to new targets for combination therapy. Here we have analyzed the early molecular response to epidermal growth factor receptor (EGFR) inhibition using RNA sequencing data covering 13,486 genes and mass spectrometry data covering 10,138 proteins. This analysis revealed a massive response to EGFR inhibition already within the first 24 h, including significant regulation of hundreds of genes known to control downstream signaling, such as transcription factors, kinases, phosphatases and ubiquitin E3-ligases. Importantly, this response included upregulation of key genes in multiple oncogenic signaling pathways that promote proliferation and survival, such as ERBB3, FGFR2, JAK3, and BCL6, indicating an early adaptive response to EGFR inhibition. Using a library of more than 500 approved and experimental compounds in a combination therapy screen, we could show that several kinase inhibitors with targets including JAK3 and FGFR2 increased the response to EGFR inhibitors. Further, we investigated the functional impact of BCL6 upregulation in response to EGFR inhibition using siRNA-based silencing of BCL6. Proteomics profiling revealed that BCL6 inhibited transcription of multiple target genes including p53, resulting in reduced apoptosis which implicates BCL6 upregulation as a new EGFR inhibitor treatment escape mechanism. Finally, we demonstrate that combined treatment targeting both EGFR and BCL6 act synergistically in killing lung cancer cells. In conclusion, or data indicates that multiple different adaptive mechanisms may act in concert to blunt the cellular impact of EGFR inhibition, and we suggest BCL6 as a potential target for EGFR inhibitor-based combination therapy.

DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis.

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

MYCN-enhanced Oxidative and Glycolytic Metabolism Reveals Vulnerabilities for Targeting Neuroblastoma.

In pediatric neuroblastoma, MYCN-amplification correlates to poor clinical outcome and new treatment options are needed for these patients. Identifying the metabolic adaptations crucial for tumor progression may be a promising strategy to discover novel therapeutic targets. Here, we have combined proteomics, gene expression profiling, functional analysis, and metabolic tracing to decipher the impact of MYCN on neuroblastoma cell metabolism. We found that high MYCN levels are correlated with altered expression of proteins involved in multiple metabolic processes, including enhanced glycolysis and increased oxidative phosphorylation. Unexpectedly, we discovered that MYCN-amplified cells showed de novo glutamine synthesis. Furthermore, inhibition of ß-oxidation reduced the viability of MYCN-amplified cells in vitro and decreased tumor burden in vivo, while not affecting non-MYCN-amplified tumors. Our data provide information on metabolic processes in MYCN expressing tumors, which could be exploited for the development of novel targeted therapies.

Author Correction: Extracellular nanovesicles released from the commensal yeast Malassezia sympodialis are enriched in allergens and interact with cells in human skin.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Breast cancer quantitative proteome and proteogenomic landscape.

In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Heterogeneity and interplay of the extracellular vesicle small RNA transcriptome and proteome.

Extracellular vesicles (EVs) mediate cell-to-cell communication by delivering or displaying macromolecules to their recipient cells. While certain broad-spectrum EV effects reflect their protein cargo composition, others have been attributed to individual EV-loaded molecules such as specific miRNAs. In this work, we have investigated the contents of vesicular cargo using small RNA sequencing of cells and EVs from HEK293T, RD4, C2C12, Neuro2a and C17.2. The majority of RNA content in EVs (49-96%) corresponded to rRNA-, coding- and tRNA fragments, corroborating with our proteomic analysis of HEK293T and C2C12 EVs which showed an enrichment of ribosome and translation-related proteins. On the other hand, the overall proportion of vesicular small RNA was relatively low and variable (2-39%) and mostly comprised of miRNAs and sequences mapping to piRNA loci. Importantly, this is one of the few studies, which systematically links vesicular RNA and protein cargo of vesicles. Our data is particularly useful for future work in unravelling the biological mechanisms underlying vesicular RNA and protein sorting and serves as an important guide in developing EVs as carriers for RNA therapeutics.

Extracellular nanovesicles released from the commensal yeast Malassezia sympodialis are enriched in allergens and interact with cells in human skin.

Malassezia sympodialis is a dominant commensal fungi in the human skin mycobiome but is also associated with common skin disorders including atopic eczema (AE). M. sympodialis releases extracellular vesicles, designated MalaEx, which are carriers of small RNAs and allergens, and they can induce inflammatory cytokine responses. Here we explored how MalaEx are involved in host-microbe interactions by comparing protein content of MalaEx with that of the parental yeast cells, and by investigating interactions of MalaEx with cells in the skin. Cryo-electron tomography revealed a heterogeneous population of MalaEx. iTRAQ based quantitative proteomics identified in total 2439 proteins in all replicates of which 110 were enriched in MalaEx compared to the yeast cells. Among the MalaEx enriched proteins were two of the M. sympodialis allergens, Mala s 1 and s 7. Functional experiments indicated an active binding and internalization of MalaEx into human keratinocytes and monocytes, and MalaEx were found in close proximity of the nuclei using super-resolution fluorescence 3D-SIM imaging. Our results provides new insights into host-microbe interactions, supporting that MalaEx may have a role in the sensitization and maintenance of inflammation in AE by containing enriched amounts of allergens and with their ability to interact with skin cells.

Time-resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3.

BACKGROUND: Regulatory T cells (Tregs) expressing the transcription factor FOXP3 are crucial mediators of self-tolerance, preventing autoimmune diseases but possibly hampering tumor rejection. Clinical manipulation of Tregs is of great interest, and first-in-man trials of Treg transfer have achieved promising outcomes. Yet, the mechanisms governing induced Treg (iTreg) differentiation and the regulation of FOXP3 are incompletely understood. RESULTS: To gain a comprehensive and unbiased molecular understanding of FOXP3 induction, we performed time-series RNA sequencing (RNA-Seq) and proteomics profiling on the same samples during human iTreg differentiation. To enable the broad analysis of universal FOXP3-inducing pathways, we used five differentiation protocols in parallel. Integrative analysis of the transcriptome and proteome confirmed involvement of specific molecular processes, as well as overlap of a novel iTreg subnetwork with known Treg regulators and autoimmunity-associated genes. Importantly, we propose 37 novel molecules putatively involved in iTreg differentiation. Their relevance was validated by a targeted shRNA screen confirming a functional role in FOXP3 induction, discriminant analyses classifying iTregs accordingly, and comparable expression in an independent novel iTreg RNA-Seq dataset. CONCLUSION: The data generated by this novel approach facilitates understanding of the molecular mechanisms underlying iTreg generation as well as of the concomitant changes in the transcriptome and proteome. Our results provide a reference map exploitable for future discovery of markers and drug candidates governing control of Tregs, which has important implications for the treatment of cancer, autoimmune, and inflammatory diseases.

Publisher Correction: Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow.

In the original version of this Article, extraneous text not belonging to the Article was accidentally appended to the results section. This error has now been corrected in both the PDF and HTML versions of the Article.

Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow.

Proteogenomics enable the discovery of novel peptides (from unannotated genomic protein-coding loci) and single amino acid variant peptides (derived from single-nucleotide polymorphisms and mutations). Increasing the reliability of these identifications is crucial to ensure their usefulness for genome annotation and potential application as neoantigens in cancer immunotherapy. We here present integrated proteogenomics analysis workflow (IPAW), which combines peptide discovery, curation, and validation. IPAW includes the SpectrumAI tool for automated inspection of MS/MS spectra, eliminating false identifications of single-residue substitution peptides. We employ IPAW to analyze two proteomics data sets acquired from A431 cells and five normal human tissues using extended (pH range, 3-10) high-resolution isoelectric focusing (HiRIEF) pre-fractionation and TMT-based peptide quantitation. The IPAW results provide evidence for the translation of pseudogenes, lncRNAs, short ORFs, alternative ORFs, N-terminal extensions, and intronic sequences. Moreover, our quantitative analysis indicates that protein production from certain pseudogenes and lncRNAs is tissue specific.

Identification of the PAK4 interactome reveals PAK4 phosphorylation of N-WASP and promotion of Arp2/3-dependent actin polymerization.

p21-activated kinase 4 (PAK4) regulates cell proliferation, apoptosis, cell motility and F-actin remodeling, but the PAK4 interactome has not been systematically analyzed. Here, we comprehensively characterized the human PAK4 interactome by iTRAQ quantitative mass spectrometry of PAK4-immunoprecipitations. Consistent with its multiple reported functions, the PAK4 interactome was enriched in diverse protein networks, including the 14-3-3, proteasome, replication fork, CCT and Arp2/3 complexes. Because PAK4 co-immunoprecipitated most subunits of the Arp2/3 complex, we hypothesized that PAK4 may play a role in Arp2/3 dependent actin regulation. Indeed, we found that PAK4 interacts with and phosphorylates the nucleation promoting factor N-WASP at Ser484/Ser485 and promotes Arp2/3-dependent actin polymerization in vitro. Also, PAK4 ablation in vivo reduced N-WASP Ser484/Ser485 phosphorylation and altered the cellular balance between G- and F-actin as well as the actin organization. By presenting the PAK4 interactome, we here provide a powerful resource for further investigations and as proof of principle, we also indicate a novel mechanism by which PAK4 regulates actin cytoskeleton remodeling.