LUHMES cells share many characteristics with human dopaminergic neurons in the substantia nigra, the cells, the demise of which is responsible for the motor symptoms in Parkinson's disease (PD). LUHMES cells can, therefore, be used bona fide as a model to study pathophysiological processes involved in PD. Previously, we showed that LUHMES cells degenerate after 6 days upon overexpression of wild-type alpha-synuclein. In the present study, we performed a transcriptome and proteome expression analysis in alpha-synuclein-overexpressing cells and GFP-expressing control cells in order to identify genes and proteins that are differentially regulated upon overexpression of alpha-synuclein. The analysis was performed 4 days after the initiation of alpha-synuclein or GFP overexpression, before the cells died, in order to identify processes that preceded cell death. After adjustments for multiple testing, we found 765 genes being differentially regulated (439 upregulated, 326 downregulated) and 122 proteins being differentially expressed (75 upregulated, 47 downregulated). In total, 21 genes and corresponding proteins were significantly differentially regulated in the same direction in both datasets, of these 13 were upregulated and 8 were downregulated. In total, 13 genes and 9 proteins were differentially regulated in our cell model, which had been previously associated with PD in recent genome-wide association studies (GWAS). In the gene ontology (GO) analysis of all upregulated genes, the top terms were "regulation of cell death," "positive regulation of programmed cell death," and "regulation of apoptotic signaling pathway," showing a regulation of cell death-associated genes and proteins already 2 days before the cells started to die. In the GO analysis of the regulated proteins, among the strongest enriched GO terms were "vesicle," "synapse," and "lysosome." In total, 33 differentially regulated proteins were associated with synapses, and 12 differentially regulated proteins were associated with the "lysosome", suggesting that these intracellular mechanisms, which had been previously associated with PD, also play an important role in our cell model.

Lysosomes are well-established as the main cellular organelles for the degradation of macromolecules and emerging as regulatory centers of metabolism. They are of crucial importance for cellular homeostasis, which is exemplified by a plethora of disorders related to alterations in lysosomal function. In this context, protein complexes play a decisive role, regulating not only metabolic lysosomal processes but also lysosome biogenesis, transport, and interaction with other organelles. Using cross-linking mass spectrometry, we analyze lysosomes and early endosomes. Based on the identification of 5376 cross-links, we investigate protein-protein interactions and structures of lysosome- and endosome-related proteins. In particular, we present evidence for a tetrameric assembly of the lysosomal hydrolase PPT1 and a heterodimeric structure of FLOT1/FLOT2 at lysosomes and early endosomes. For FLOT1-/FLOT2-positive early endosomes, we identify >300 putative cargo proteins and confirm eleven substrates for flotillin-dependent endocytosis, including the latrophilin family of adhesion G protein-coupled receptors.

(1) Background: Neuroblastomas (NBs) are the most common extracranial solid tumors of children. The amplification of the Myc-N proto-oncogene (MYCN) is a major driver of NB aggressiveness, while high expression of the neurotrophin receptor NTRK1/TrkA is associated with mild disease courses. The molecular effects of NTRK1 signaling in MYCN-amplified NB, however, are still poorly understood and require elucidation. (2) Methods: Inducible NTRK1 expression was realized in four NB cell lines with (IMR5, NGP) or without MYCN amplification (SKNAS, SH-SY5Y). Proteome and phosphoproteome dynamics upon NTRK1 activation by its ligand, NGF, were analyzed in a time-dependent manner in IMR5 cells. Target validation by immunofluorescence staining and automated image processing was performed using the three other NB cell lines. (3) Results: In total, 230 proteins and 134 single phosphorylated class I phosphosites were found to be significantly regulated upon NTRK1 activation. Among known NTRK1 targets, Stathmin and the neurosecretory protein VGF were recovered. Additionally, we observed the upregulation and phosphorylation of Lamin A/C (LMNA) that accumulated inside nuclear foci. (4) Conclusions: We provide a comprehensive picture of NTRK1-induced proteome and phosphoproteome dynamics. The phosphorylation of LMNA within nucleic aggregates was identified as a prominent feature of NTRK1 signaling independent of the MYCN status of NB cells.

Summary:Because of the steadily increasing and already manually unmanageable total number of biomarker-related articles in biomedical research, there is a need for intelligent systems that extract all relevant information from biomedical texts and provide it as structured information to researchers in a user-friendly way. To address this, BIONDA was implemented as a free text mining-based online database for molecular biomarkers including genes, proteins and miRNAs and for all kinds of diseases. The contained structured information on published biomarkers is extracted automatically from Europe PMC publication abstracts and high-quality sources like UniProt and Disease Ontology. This allows frequent content updates.Availability and implementation:BIONDA is freely accessible via a user-friendly web application at http://bionda.mpc.ruhr-uni-bochum.de. The current BIONDA code is available at GitHub via https://github.com/mpc-bioinformatics/bionda.Supplementary information:Supplementary data are available at Bioinformatics Advances online.

Biomarkers have been the focus of research for more than 30 years [REF1]. Paone et al. were among the first scientists to use the term biomarker in the course of a comparative study dealing with breast carcinoma [REF2]. In recent years, in addition to proteins and genes, miRNA or micro RNAs, which play an essential role in gene expression, have gained increased interest as valuable biomarkers. As a result, more and more information on miRNA biomarkers can be extracted via text mining approaches from the increasing amount of scientific literature. In the late 1990s the recognition of specific terms in biomedical texts has become a focus of bioinformatic research to automatically extract knowledge out of the increasing number of publications. For this, amongst other methods, machine learning algorithms are applied. However, the recognition (classification) capability of terms by machine learning or rule based algorithms depends on their correct and reproducible training and development. In the case of machine learning-based algorithms the quality of the available training and test data is crucial. The algorithms have to be tested and trained with curated and trustable data sets, the so-called gold or silver standards. Gold standards are text corpora, which are annotated by expertes, whereby silver standards are curated automatically by other algorithms. Training and calibration of neural networks is based on such corpora. In the literature there are some silver standards with approx. 500,000 tokens [REF3]. Also there are already published gold standards for species, genes, proteins or diseases. However, there is no corpus that has been generated specifically for miRNA. To close this gap, we have generated GoMi, a novel and manually curated gold standard corpus for miRNA. GoMi can be directly used to train ML-methods to calibrate or test different algorithms based on the rule-based approach or dictionary-based approach. The GoMi gold standard corpus was created using publicly available PubMed abstracts. GoMi can be downloaded here: https://github.com/mpc-bioinformatics/mirnaGS---GoMi.

The analysis of disease-related changes in the phosphorylation status of cellular signal transduction networks is of major interest to biomedical researchers. Mass spectrometry-based proteomics allows the analysis of phosphorylation in a global manner. However, several technical challenges need to be addressed when the phosphorylation of proteins is analyzed. Low-abundant phosphopeptides need to be enriched before analysis, thereby introducing additional steps in sample preparation. Consequently, the applied quantification strategies should be robust towards elaborate sampling handling, rendering label-based quantification strategies the methods of choice in many experiments. Here, we present a protocol for SILAC labeling and the subsequent isolation of phosphopeptides using TiO2 affinity chromatography. We outline the corresponding LC-MS/MS analysis and the essential steps of data processing.

The service center BioInfra.Prot, which is part of the German Network for Bioinformatics Infrastructure (de.NBI) and ELIXIR Germany, provides a comprehensive portfolio of bioinformatics services for proteomics. This includes data standardization, data conversion and data publication services as well as computational and statistical consultancy and data analysis (esp. protein inference and expression analysis), software tools (e.g. PIA and PAA) and training courses. Altogether, we offer a comprehensive proteomics service portfolio. Recently, we extended our service list by a tool based on biomedical text mining: The novel biomarker database BIONDA, which provides structured information on biomarker-disease relationships of all biomarker candidates published in PubMed abstracts. Since there is a dramatically increasing number of biomarker-related scientific articles and structured information on biomarkers is often restricted, limited to particular types of markers or diseases, outdated and/or user-unfriendly in existing online databases, it becomes unfeasible for researchers to keep track of all recent biomarker publications. In this context, the objective of BIONDA is to acquire the current knowledge on biomarker-disease relationships automatically by an intelligent system, which provides it freely and rapidly. As sources for BIONDA’s database entries, we use besides PubMed article abstracts further renowned databases such as UniProt and Human Disease Ontology. This information is acquired automatically and updated regularly using text mining methods. BIONDA is available freely via a user-friendly web interface and it is not limited to any kind of diseases. As a specific characteristic, BIONDA’s database entries are rated by a scoring approach estimating biomarker reliability. Thus, BIONDA is a valuable knowledge resource for biomedical research.

Targeted proteomics techniques allow accurate quantitative measurements of analytes in complex matrices with dynamic linear ranges that span up to 4-5 orders of magnitude. Hence, targeted methods are promising for the development of robust protein assays in several sensitive areas, for example, in health care. However, exploiting the full method potential requires reliable determination of the dynamic range along with related quantification limits for each analyte. Here, a software named CalibraCurve that enables an automated batch-mode determination of dynamic linear ranges and quantification limits for both targeted proteomics and similar assays is presented. The software uses a variety of measures to assess the accuracy of the calibration, namely precision and trueness. Two different kinds of customizable graphs are created (calibration curves and response factor plots). The accuracy measures and the graphs offer an intuitive, detailed, and reliable opportunity to assess the quality of the model fit. Thus, CalibraCurve is deemed a highly useful and flexible tool to facilitate the development and control of reliable SRM/MRM-MS-based proteomics assays.

The pathological hallmark of Parkinson's disease (PD) is the loss of neuromelanin-containing dopaminergic neurons within the substantia nigra pars compacta (SNpc). Additionally, numerous studies indicate an altered synaptic function during disease progression. To gain new insights into the molecular processes underlying the alteration of synaptic function in PD, a proteomic study was performed. Therefore, synaptosomes were isolated by density gradient centrifugation from SNpc tissue of individuals at advanced PD stages (N = 5) as well as control subjects free of pathology (N = 5) followed by mass spectrometry-based analysis. In total, 362 proteins were identified and assigned to the synaptosomal core proteome. This core proteome comprised all proteins expressed within the synapses without regard to data analysis software, gender, age, or disease. The differential analysis between control subjects and PD cases revealed that CD9 antigen was overrepresented and fourteen proteins, among them Thymidine kinase 2 (TK2), mitochondrial, 39S ribosomal protein L37, neurolysin, and Methionine-tRNA ligase (MARS2) were underrepresented in PD suggesting an alteration in mitochondrial translation within synaptosomes.

We evaluated the quantification strategies label-free (LF), stable isotope labeling by amino acids in cell culture (SILAC), and tandem mass tags (TMT) and their performance in quantification of proteins and phosphosites (p-sites) to identify the most powerful approach for monitoring cellular signaling. We analyzed the epidermal growth factor receptor (EGFR) signaling network, which plays an essential role in colorectal cancer, and studied its dynamics within 24 h upon treatment with the EGFR-blocking antibody cetuximab, representing the first cellular adaption toward therapy. LF achieved superior coverage but was outperformed by label-based approaches regarding technical variability, especially for quantification of p-sites. TMT showed the lowest coverage and most missing values. We found that its performance considerably decreases when experimental replicates are distributed over several TMT plexes. SILAC showed the highest precision and outstanding performance for quantification of p-sites, rendering it the method of choice for analyzing cellular signaling in cell culture models. On the protein level, we observed only little regulation upon cetuximab treatment, whereas a great fraction of p-sites was significantly regulated. These dynamics represented an initial downregulation of the MAPK pathway, which was partially rescued as early as 24 h after treatment. We identified upregulation and signaling via ERBB3 as well as calcium and cAMP signaling as possible mechanisms bypassing the blockage of EGFR.

Upon stimulation of cells with transforming growth factor β (TGF-β), Smad proteins form trimeric complexes and activate a broad spectrum of target genes. It remains unresolved which of the possible Smad complexes are formed in cellular contexts and how these contribute to gene expression. By combining quantitative mass spectrometry with a computational selection strategy, we predict and provide experimental evidence for the three most relevant Smad complexes in the mouse hepatoma cell line Hepa1-6. Utilizing dynamic pathway modeling, we specify the contribution of each Smad complex to the expression of representative Smad target genes, and show that these contributions are conserved in human hepatoma cell lines and primary hepatocytes. We predict, based on gene expression data of patient samples, increased amounts of Smad2/3/4 proteins and Smad2 phosphorylation as hallmarks of hepatocellular carcinoma and experimentally verify this prediction. Our findings demonstrate that modeling approaches can disentangle the complexity of transcription factor complex formation and its impact on gene expression.

Rationale: Signal transduction mediated by receptor tyrosine kinases (RTKs) such as EGFR and Met is often altered in lung cancer. Targeting RTKs in such tumors offers a benefit, however it is followed by a fast resistance. Therefore, in-depth mechanistic understanding of how tumor cells process the information from RTKs is essential for developing novel intervention strategies. Aim: We aim to understand whether Met receptor clearance from the plasma membrane and both, degree and duration of receptor phosphorylation could be modulated to encode the information about the stimulus. We aim to characterize changes in the proteome upon different stimuli and using mathematical modelling gain insight into the mechanisms of pathway activation. Methods: Mass Spectrometry and microscopy are used to examine perturbations induced by antibodies selectively recognizing Met, or Archazolid A which accumulates receptors in intracellular vesicles. Results: Applied antibodies differentially induce the residue-specific patterns of Met phosphorylation. As a result we observe alterations in activation of downstream signalling pathways and changes in the invasive potential of lung cancer cells. HGF stimulation after treatment with Archazolid A leads to a faster induction and quicker attenuation of Met activation and phosphorylation of downstream signalling pathways is impaired. Conclusion: Met autophosphorylation degree in response to the extracellular cues depends on the character of a stimulus. By modulating the phosphorylation of different Met residues we are able to alter cell fate decisions. Applying systems biology approach we hope to descibe the mechanistic properties of different receptor activation patterns.

The same pathway, such as the mitogen-activated protein kinase (MAPK) pathway, can produce different cellular responses, depending on stimulus or cell type. We examined the phosphorylation dynamics of the MAPK kinase MEK and its targets extracellular signal-regulated kinase 1 and 2 (ERK1/2) in primary hepatocytes and the transformed keratinocyte cell line HaCaT A5 exposed to either hepatocyte growth factor or interleukin-6. By combining quantitative mass spectrometry with dynamic modeling, we elucidated network structures for the reversible threonine and tyrosine phosphorylation of ERK in both cell types. In addition to differences in the phosphorylation and dephosphorylation reactions, the HaCaT network model required two feedback mechanisms, which, as the experimental data suggested, involved the induction of the dual-specificity phosphatase DUSP6 and the scaffold paxillin. We assayed and modeled the accumulation of the double-phosphorylated and active form of ERK1/2, as well as the dynamics of the changes in the monophosphorylated forms of ERK1/2. Modeling the differences in the dynamics of the changes in the distributions of the phosphorylated forms of ERK1/2 suggested that different amounts of MEK activity triggered context-specific responses, with primary hepatocytes favoring the formation of double-phosphorylated ERK1/2 and HaCaT A5 cells that produce both the threonine-phosphorylated and the double-phosphorylated form. These differences in phosphorylation distributions explained the threshold, sensitivity, and saturation of the ERK response. We extended the findings of differential ERK phosphorylation profiles to five additional cultured cell systems and matched liver tumor and normal tissue, which revealed context-specific patterns of the various forms of phosphorylated ERK.

Recombinant human erythropoietin (rhEPO) is an important CHO cell-derived glycoprotein and the degree of sialylation of this hormone is crucial for its in vivo bioactivity. In order to improve the purification process serotonin as a potential affinity ligand was tested for preparative chromatographic separation of rhEPO glycoforms into fractions of different degrees of sialylation. Therefore, two chromatographic matrices were prepared by immobilizing serotonin on CNBr- and NHS-Sepharose™. First it was shown both matrices bind rhEPO only in its sialylated form. Results indicate that binding is pH independent between pH 3.5 to 8 suggesting it is not only based on electrostatic interactions. Second, after optimal binding conditions were identified, semi-purified rhEPO was loaded onto both matrices and eluted using a stepwise elution gradient of sodium chloride. For comparison same affinity purification experiments were performed using wheat germ agglutinin-coupled agarose, a lectin known for its affinity towards sialylated glycoproteins. To monitor changes in N-glycan fingerprint, eluate fractions were analyzed by multiplexed capillary gel electrophoresis coupled to laser-induced fluorescence (xCGE-LIF). For the serotonin matrices an increasing degree of sialylation was observed from the first to the third elution fraction while purity of rhEPO could be increased at the same time. The late elution fractions of serotonin-coupled CNBr- and NHS-Sepharose™ also showed an overall sialylation degree exceeding that of the starting material. In contrast, for rhEPO bound to wheat germ agglutinin-coupled agarose, no distinct change in the degree of sialylation could be observed after elution. Overall, these encouraging results highlight the potential of serotonin as a chromatographic ligand for the improvement of pharmaceutical purification processes of rhEPO.

In systems biology, quantitative experimental data is the basis of building mathematical models. In most of the cases, they are stored in Excel files and hosted locally. To have a public database for collecting, retrieving and citing experimental raw data as well as experimental conditions is important for both experimentalists and modelers. However, the great effort needed in the data handling procedure and in the data submission procedure becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding the database to deliver its benefit. Moreover, manual copy and paste operations which are commonly used in those procedures increase the chance of making mistakes. Excemplify, a web-based application, proposes a flexible and adaptable template-based solution to solve these problems. Comparing to the normal template based uploading approach, which is supported by some public databases, rather than predefining a format that is potentiall impractical, Excemplify allows users to create their own experiment-specific content templates in different experiment stages and to build corresponding knowledge bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is able to parse experimental data from the initial setup stage and generate following stages spreadsheets automatically. The proposed solution standardizes the flows of data traveling according to the standard procedures of applying the experiment, cuts down the amount of manual effort and reduces the chance of mistakes caused by manual data handling. In addition, it maintains the context of meta-data from the initial preparation manuscript and improves the data consistency. It interoperates and complements RightField and SEEK as well.