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Metabolic diseases are closely linked to aberrant synthesis of endogenous fatty acids in the liver, called de novo lipogenesis (DNL), which is mediated by the enzyme fatty acid synthase (FASN). The composition of complex lipids consists of saturated or monosaturated fatty acids, which can be endogenously produced, and polyunsaturated fatty acids (PUFA), which are strictly dietary. Compositional differences between individuals are insufficiently understood and may influence the onset and progression of metabolic and cardiovascular diseases. Here we show that DNL critically determines the use of dietary PUFA. A patient with a hypofunctional heterozygous de novo Arg2177Cys variant in FASN exhibited an elevated composition of PUFA, which was phenocopied by pharmacological inhibition of FASN with TVB-2640 in patients with nonalcoholic steatohepatitis (NASH). In mice, the incorporation rate of supplemented omega-3 PUFA during an obesogenic diet was increased by genetic or pharmacologic reduction of DNL. Mechanistically, we show that the FASN variant exhibited a cysteine-dependent, non-enzymatic acetylation of FASN, which resulted in hyperubiquitinylation and decreased protein stability. The study reveals that PUFA storage is an active, enzymatic process controlled by FASN, DGAT2 and MFSD2A and that combining FASN inhibition and PUFA supplementation exerts additive beneficial metabolic effects. These findings provide evidence that the success of PUFA supplementation may depend on the rate of endogenous DNL and that combined PUFA supplementation and FASN inhibition may be a promising approach in metabolic disease.

Fractionation of proteoforms is currently the most challenging topic in the field of protein purification. The need for considering the existence of proteoforms into experimental approaches is not only important in Life Science research in general but especially in the manufacturing of therapeutic proteins (TPs) like recombinant therapeutic antibodies (mAbs). Some of the proteoforms of TPs have significantly decreased actions or even cause side effects. The identification and removal of proteoforms differing from the main species, having the desired action, is challenging because the difference in the composition of atoms often is very small and their concentration in comparison to the main proteoform can be small. In this study we demonstrate that sample displacement batch chromatography (SDBC) is an easy to handle, economic and efficient method for fractionating proteoforms. As a model sample a commercial ovalbumin fraction was used, containing many ovalbumin proteoforms. The most promising parameters for the SDBC were determined by a screening approach and applied for a 10-segment fractionation of the ovalbumin with cation exchange chromatography resin. Mass spectrometry of intact proteoforms was used for characterizing the SDBC fractionation process. By SDBC a significant separation of different proteoforms was obtained.

SummaryMedulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically very heterogenous. To unravel phenotypically relevant MB subtypes, we compiled a harmonized proteome dataset of 167 MBs and integrated findings with DNA methylation and N-glycome data. Six proteome MB subtypes emerged, that could be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pGroup3-Myc), and synapses/immunological processes (pSHHs, pGroup3 and pGroup4). Multiomic analysis revealed different conservation levels of proteome features across MB subtypes at the DNA-methylation level. Aggressive pGroup3-Myc MBs and favorable pWNT MBs were most similar in cluster hierarchies concerning overall proteome patterns but showed different protein abundances of the vincristine resistance associated multiprotein complex TriC/CCT and of N-glycan turnover associated factors. The N-glycome reflected proteome subtypes and complex-bisecting N-glycans characterized pGroup3-Myc tumors. Our results shed light on new targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.SignificanceWhereas the application of omics technologies has significantly improved MB tumor classification and treatment stratification, it is still of debate, which features predict best clinical outcome. Moreover, treatment options - especially for high-risk groups - are still unsatisfactory. In contrast to nucleic acids, the proteome and their N-glycans may reflect the phenotype of a tumor in a more direct way and thus hold the potential to discover clinically relevant phenotypes and potentially targetable pathways. We show that these analyses are feasible on formalin fixed and paraffine embedded tissue. Compiling a comprehensive MB dataset, we detected new biomarkers and characteristics for high- and low-risk MB subtypes that were not reflected by other omic data modalities before. Specifically, we identified subtype specific abundance differences in proteins of the vincristine resistance associated multiprotein complex TriC/CCT and in proteins involved in N-glycan turnover. Changes in the N-glycans are considered as potential hallmarks of cancer and we show that N-glycan profiles can distinguish MB subtypes. These tumor-specific N-glycan structures hold a strong potential as new biomarkers, as well as immunotherapy targets.Highlights- Integration of in-house proteome data on formalin fixated paraffine embedded medulloblastoma (MB) and publicly available datasets enables large scale proteome analysis of MB- Six proteome MB subtypes can be assigned to two main molecular programs: replication/ translation versus synapse/immune system- Identification and validation of IHC compatible protein-biomarkers for high and low risk MB subtypes, such as TNC and PALMD.- Subtype specific correlation of the DNA methylome and the proteome reveals different conserved molecular characteristics across MB subtypes.- pGroup3-Myc subtype MBs are associated with high-risk features including high abundances of vincristine resistance associated TriC/CCT member proteins- Proteome MB subtypes show differential N-glycosylation patterns, revealing complex-bisecting glycans as potentially immunotargetable hallmarks of the high risk pGroup3-Myc subtype.

Background Despite recent progress in liquid biopsy technologies, early blood-based detection of breast cancer is still a challenge. Methods We analyzed secretion of the protein cellular communication network factor 1 (CCN1, formerly cysteine-rich angiogenic inducer 61) in breast cancer cell lines by an enzyme-linked immunosorbent assay (ELISA). Soluble CCN1 in the plasma (2.5 µL) of 544 patients with breast cancer and 427 healthy controls was analyzed by ELISA. The breast cancer samples were acquired at the time of primary diagnosis prior to neoadjuvant therapy or surgery. A classifier was established on a training cohort of patients with breast cancer and age-adapted healthy controls and further validated on an independent cohort comprising breast cancer patients and healthy controls. Samples from patients with benign breast diseases were investigated as additional controls. Samples from patients with acute heart diseases (n = 127) were investigated as noncancer controls. The diagnostic accuracy was determined by receiver operating characteristic using the parameters area under the curve, sensitivity, and specificity. Results CCN1 was frequently secreted by breast cancer cell lines into the extracellular space. Subsequent analysis of clinical blood samples from patients with breast cancer and age-adjusted healthy controls revealed an overall specificity of 99.0% and sensitivity of 80.0% for cancer detection. Remarkably, 81.5% of small T1 cancers were already CCN1-positive, while CCN1 concentrations in patients with benign breast lesions were below the threshold for breast cancer detection. Conclusions Circulating CCN1 is a potentially novel blood biomarker for the detection of breast cancer at the earliest invasive stage.

Purpose Psychosocial screening for glioma patients is challenging because many patients suffer from neurocognitive deficits, which may impair assessment. This study’s aim was to exploratively develop three screening questions for unmet needs to prospectively be applicable in patient–doctor consultation. Methods Patient interviews, a survey for health-care professionals and a weighted scoring procedure were developed for this study. Six main areas were defined according to main areas of validated questionnaires (psyche, cognition, body, role functioning, social support, unmet needs). Patients and health-care professionals rated the importance of these areas and corresponding items, patients additionally stated whether the issues addressed affected them. Results A total of 50 patients were included, and 36 health-care professionals participated in the online survey. The three areas (psyche, body and cognition) considered to be most relevant by both, health-care professionals and patients, generated three screening questions. If the patient was affected by the issue addressed with a screening question, a subordinate question from that area that our patient sample considered most important could additionally be asked. The elaborated screening questions are the following: (1) main area psyche: “Has your mood worsened?”, (2) main area body: “Do physical changes put a strain on you?”, and (3) main area cognition: “Has your memory capacity worsened?” Conclusion These questions represent a basis for further research regarding their application in neuro-oncological clinical routine.

For investigating the molecular physiology and pathophysiology in organs, the most exact data should be obtained; if not, organ-specific cell lines are analyzed, or the whole organ is homogenized, followed by the analysis of its biomolecules. However, if the morphological organization of the organ can be addressed, then, in the best case, the composition of molecules in single cells of the target organ can be analyzed. Laser capture microdissection (LCM) is a technique which enables the selection of specific cells of a tissue for further analysis of their molecules. However, LCM is a time-consuming two-dimensional technique, and optimal results are only obtained if the tissue is fixed, e.g., by formalin. Especially for proteome analysis, formalin fixation reduced the number of identifiable proteins, and this is an additional drawback. Recently, it was demonstrated that sampling of fresh-frozen (non-fixed) tissue with an infrared-laser is giving higher yields with respect to the absolute protein amount and number of identifiable proteins than conventional mechanical homogenization of tissues. In this study, the applicability of the infrared laser tissue sampling for the proteome analysis of different cell layers of murine intestine was investigated, using LC–MS/MS-based differential quantitative bottom-up proteomics. By laser ablation, eight consecutive layers of colon tissue were obtained and analyzed. However, a clear distinguishability of protein profiles between ascending, descending, and transversal colon was made, and we identified the different intestinal-cell-layer proteins, which are cell-specific, as confirmed by data from the Human Protein Atlas. Thus, for the first time, sampling directly from intact fresh-frozen tissue with three-dimensional resolution is giving access to the different proteomes of different cell layers of colon tissue.

Intraocular medulloepithelioma (IO-MEPL) is a rare embryonal ocular neoplasm, prevalently occurring in children. IO-MEPLs share histomorphological features with CNS embryonal tumors with multilayered rosettes (ETMRs), referred to as intracranial medulloepitheliomas. While Sonic hedgehog (SHH) and WNT signaling pathways are crucial for ETMR pathogenesis, the impact of these pathways on human IO-MEPL development is unclear. Gene expression analyses of human embryonal tumor samples revealed similar gene expression patterns and significant overrepresentation of SHH and WNT target genes in both IO-MEPL and ETMR. In order to unravel the function of Shh and Wnt signaling for IO-MEPL pathogenesis in vivo, both pathways were activated in retinal precursor cells in a time point specific manner. Shh and Wnt co-activation in early Sox2- or Rax-expressing precursor cells resulted in infiltrative ocular lesions that displayed extraretinal expansion. Histomorphological, immunohistochemical, and molecular features showed a strong concordance with human IO-MEPL. We demonstrate a relevant role of WNT and SHH signaling in IO-MEPL and report the first mouse model to generate tumor-like lesions with features of IO-MEPL. The presented data may be fundamental for comprehending IO-MEPL initiation and developing targeted therapeutic approaches.

Truffles of the Tuber species are known as expensive foods, mainly for their distinct aroma and taste. This high price makes them a profitable target of food fraud, e.g., the misdeclaration of cheaper truffle species as expensive ones. While many studies investigated truffles on the metabolomic level or the volatile organic compounds extruded by them, research at the proteome level as a phenotype determining basis is limited. In this study, a bottom-up proteomic approach based on LC-MS/MS measurements in data-independent acquisition mode was performed to analyze the truffle species Tuber aestivum, Tuber albidum pico, Tuber indicum, Tuber magnatum, and Tuber melanosporum, and a protein atlas of the investigated species was obtained. The yielded proteomic fingerprints are unique for each of the of the five truffle species and can now be used in case of suspected food fraud. First, a comprehensive spectral library containing 9000 proteins and 50,000 peptides was generated by two-dimensional liquid chromatography coupled to tandem mass spectrometry (2D-LC-MS/MS). Then, samples of the truffle species were analyzed in data-independent acquisition (DIA) proteomics mode yielding 2715 quantified proteins present in all truffle samples. Individual species were clearly distinguishable by principal component analysis (PCA). Quantitative proteome fingerprints were generated from 2066 ANOVA significant proteins, and side-by-side comparisons of truffles were done by T-tests. A further aim of this study was the annotation of functions for the identified proteins. For Tuber magnatum and Tuber melanosporum conclusive links to their superior aroma were found by enrichment of proteins responsible for sulfur-metabolic processes in comparison with other truffles. The obtained data in this study may serve as a reference library for food analysis laboratories in the future to tackle food fraud by misdeclaration of truffles. Further identified proteins with their corresponding abundance values in the different truffle species may serve as potential protein markers in the establishment of targeted analysis methods. Lastly, the obtained data may serve in the future as a basis for deciphering the biochemistry of truffles more deeply as well, when protein databases of the different truffle species will be more complete.

It was recently shown that ultrashort pulse infrared (IR) lasers, operating at the wavelength of the OH vibration stretching band of water, are highly efficient for sampling and homogenizing biological tissue. In this study we utilized a tunable nanosecond infrared laser (NIRL) for tissue sampling and homogenization with subsequent liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis for mass spectrometric proteomics. For the first time, laser sampling was performed with murine spleen and colon tissue. An ablation volume of 1.1 × 1.1 × 0.4 mm³ (approximately 0.5 µL) was determined with optical coherence tomography (OCT). The results of bottom-up proteomics revealed proteins with significant abundance differences for both tissue types, which are in accordance with the corresponding data of the Human Protein Atlas. The results demonstrate that tissue sampling and homogenization of small tissue volumes less than 1 µL for subsequent mass spectrometric proteomics is feasible with a NIRL.

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.

The characterization of therapeutic glycoproteins is challenging due to the structural heterogeneity of the therapeutic protein glycosylation. This study presents an in-depth analytical strategy for glycosylation of first-generation erythropoietin (epoetin beta), including a developed mass spectrometric workflow for N-glycan analysis, bottom-up mass spectrometric methods for site-specific N-glycosylation, and a LC-MS approach for O-glycan identification. Permethylated N-glycans, peptides, and enriched glycopeptides of erythropoietin were analyzed by nanoLC-MS/MS, and de-N-glycosylated erythropoietin was measured by LC-MS, enabling the qualitative and quantitative analysis of glycosylation and different glycan modifications (e.g., phosphorylation and O-acetylation). The newly developed Python scripts enabled the identification of 140 N-glycan compositions (237 N-glycan structures) from erythropoietin, especially including 8 phosphorylated N-glycan species. The site-specificity of N-glycans was revealed at the glycopeptide level by pGlyco software using different proteases. In total, 114 N-glycan compositions were identified from glycopeptide analysis. Moreover, LC-MS analysis of de-N-glycosylated erythropoietin species identified two O-glycan compositions based on the mass shifts between non-O-glycosylated and O-glycosylated species. Finally, this integrated strategy was proved to realize the in-depth glycosylation analysis of a therapeutic glycoprotein to understand its pharmacological properties and improving the manufacturing processes.

The characterization of glycoproteins, like erythropoietin, is challenging due to the structural micro- and macro-heterogeneity of the protein glycosylation. This study presents an in-depth strategy for glycosylation analysis of a first-generation erythropoietin (epoetin beta), including a developed top-down mass spectrometric workflow for N-glycan analysis, bottom-up mass spectrometric methods for site-specific N-glycosylation and a LC-MS approach for O-glycan identification. Permethylated N-glycans, peptides and enriched glycopeptides of erythropoietin were analyzed by nanoLC-MS/MS and de-N-glycosylated erythropoietin was measured by LC-MS, enabling the qualitative and quantitative analysis of glycosylation and different glycan modifications (e.g., phosphorylation and O-acetylation). Extending the coverage of our newly developed Python script to phosphorylated N-glycans enabled the identification of 140 N-glycan compositions (237 N-glycan structures) from erythropoietin. The site-specificity of N-glycans was revealed at glycopeptide level by pGlyco software using different proteases. In total, 215 N-glycan compositions were identified from N-glycan and glycopeptide analysis. Moreover, LC-MS analysis of de-N-glycosylated erythropoietin species identified two different O-glycan compositions, based on the mass shifts between non-O-glycosylated and O-glycosylated species. This integrated strategy allows the in-depth glycosylation analysis of a therapeutic glycoprotein to understand its pharmacological properties and improving the manufacturing processes.

Dataset integration is common practice to overcome limitations, e.g., in statistically underpowered omics datasets. This is of particular importance when analyzing rare tumor entities. However, combining datasets leads to the introduction of biases, so called 'batch effects', which are due to differences in quantification techniques, laboratory equipment or used tissue type. A common problem is the missing quantification for features like gene transcripts or proteins within a dataset. These missing values can appear at random in a given dataset and also get introduced by combination of multiple datasets. Currently, strategies beyond common normalization for batch effect reduction are either missing entirely or are unable to handle absence of data points and therefore rely on error-prone data imputation. We introduce a framework that enables batch effect adjustments for combined datasets while avoiding data loss by appropriately handling missing values without imputation. The underlying idea is based on a matrix dissection approach, adjusting common information from the integrated dataset under guarantee of sufficient data presence. The strategy is implemented within the R environment and linked with popular software stacks that are built on top of R. Successful data adjustment is exemplarily shown for proteomic data generated by different quantification approaches and LC-MS/MS instrumentation setups.

Global in-depth analysis of N-glycosylation, as the most complex post-translational modification of proteins, is requiring methods being as sensitive, selective and reliable as possible. Here, an enhanced strategy for N-glycomics is presented comprising optimized sample preparation yielding enhanced glycoprotein recovery and permethylation efficiency, isotopic labelling for data quality control and relative quantification, integration of new N-glycan libraries (human and mouse), newly developed R-scripts matching experimental MS1 data to theoretical N-glycan compositions and bundled sequencing algorithms for MS2-based structural identification to ultimately enhance the coverage and accuracy of N-glycans. With this strategy the numbers of identified N-glycans are more than doubled compared with previous studies, exemplified by etanercept (more than 3-fold) and chicken ovalbumin (more than 2-fold) at nanogram level. The power of this strategy and applicability to biological samples is further demonstrated by comparative N-glycomics of human acute promyelocytic leukemia cells before and after treatment with all-trans retinoic acid, showing that N-glycan biosynthesis is slowed down and 57 species are significantly altered in response to the treatment. This improved analytical platform enables deep and accurate N-glycomics for glycobiological research and biomarker discovery.

Colorectal cancer (CRC) patients suffer from the second highest mortality among all cancer entities. In half of all CRC patients, colorectal cancer liver metastases (CRLM) can be observed. Metastatic colorectal cancer is associated with poor overall survival and limited treatment options. Even after successful surgical resection of the primary tumor, metachronous liver metastases occur in one out of eight cases. The only available curative intended treatment is hepatic resection, but metachronous CRLM frequently recur after approximately 1 year. In this study, we performed a proteome analysis of three recurrent liver metastases of a single CRC patient by mass spectrometry. Despite surgical resection of the primary CRC and adjuvant chemotherapy plus cetuximab treatment, the patient developed three metachronous CRLM which occurred consecutively after 9, 21 and 31 months. We identified a set of 1132 proteins expressed in the three metachronous CRLM, of which 481 were differentially regulated, including 81 proteins that were associated with the extracellular matrix (ECM). 56 ECM associated proteins were identified as upregulated in the third metastasis, 26 (46%) of which were previously described as negative prognostic markers in CRC, including tenascin C, nidogen 1, fibulin 1 and vitronectin. These data may reflect an ascending trend of malignancy from the first to the third metachronous colorectal cancer liver metastasis. Additionally, the results indicate different ECM phenotypes for recurrent metachronous metastasis, associated with different grades of malignancy and highlights the importance of individual analysis of molecular features in different, consecutive metastatic events in a single patient. Electronic supplementary material The online version of this article (10.1007/s10585-020-10058-8) contains supplementary material, which is available to authorized users.

Proteomics and genomics discovery experiments generate increasingly large result tables, necessitating more researcher time to convert the biological data into new knowledge. Literature review is an important step in this process and can be tedious for large scale experiments. An informed and strategic decision about which biomolecule targets should be pursued for follow-up experiments thus remains a considerable challenge. To streamline and formalise this process of literature retrieval and analysis of discovery based &lsquo, omics data and as a decision-facilitating support tool for follow-up experiments we present OmixLitMiner, a package written in the computational language R. The tool automates the retrieval of literature from PubMed based on UniProt protein identifiers, gene names and their synonyms, combined with user defined contextual keyword search (i.e., gene ontology based). The search strategy is programmed to allow either strict or more lenient literature retrieval and the outputs are assigned to three categories describing how well characterized a regulated gene or protein is. The category helps to meet a decision, regarding which gene/protein follow-up experiments may be performed for gaining new knowledge and to exclude following already known biomarkers. We demonstrate the tool&rsquo, s usefulness in this retrospective study assessing three cancer proteomics and one cancer genomics publication. Using the tool, we were able to corroborate most of the decisions in these papers as well as detect additional biomolecule leads that may be valuable for future research.

Epithelial ovarian carcinoma (EOC) is considered the deadliest gynecological cancer, largely due to the fact that it is often diagnosed once the cancer has already metastasized, thus making the disease more difficult to treat. Throughout metastasis, ovarian epithelial cancer cells must overcome many feats, including surviving in extracellular matrix (ECM) detachment. ECM-detached cancer cells must evade a number of insults, including increased intracellular reactive oxygen species (ROS). Recent evidence suggests ECM-detached cancer cells rely on antioxidant enzymes to combat these increasing levels of ROS to promote survival; however, the specific antioxidant enzymes involved in this process have yet to be fully elucidated. Sestrin 2 (SESN2) is a multi-functional protein that has been found to be instrumental in many different signaling pathways; notably, it has been recognized to play a critical role in eliminating ROS. Here, we show that SESN2 plays a unique role in maintaining the viability of ECM-detached metastatic ovarian epithelial cancer cells, and elimination of this critical protein results in compromised viability. Thus, these data identify SESN2 as a potentially interesting therapeutic target for treating this deadly metastatic disease.