Wrangling phosphoproteomic data to elucidate cancer signaling pathways

Craniofacial chondrogenesis in organoids from human stem cell-derived neural crest cells

Knowledge of cell signaling pathways that drive human neural crest differentiation into craniofacial chondrocytes is incomplete, yet essential for using stem cells to regenerate craniomaxillofacial structures. To accelerate translational progress, we developed a differentiation protocol that generated self-organizing craniofacial cartilage organoids from human embryonic stem cell-derived neural crest stem cells. Histological staining of cartilage organoids revealed tissue architecture and staining typical of elastic cartilage. Protein and post-translational modification (PTM) mass spectrometry and snRNA-seq data showed that chondrocyte organoids expressed robust levels of cartilage extracellular matrix (ECM) components: many collagens, aggrecan, perlecan, proteoglycans, and elastic fibers. We identified two populations of chondroprogenitor cells, mesenchyme cells and nascent chondrocytes, and the growth factors involved in paracrine signaling between them. We show that ECM components secreted by chondrocytes not only create a structurally resilient matrix that defines cartilage, but also play a pivotal autocrine cell signaling role in determining chondrocyte fate.

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer

We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy.

Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data

Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.

Proteomics analysis identifies PEA-15 as an endosomal phosphoprotein that regulates α5β1 integrin endocytosis

Endosomal trafficking of cell surface receptors is essential to their function. Integrins are transmembrane receptors that integrate adhesion to the extracellular matrix with engagement of the cytoskeleton. Ligated integrins mediate diverse signals that regulate matrix assembly, cell survival, cell morphology, and cell motility. Endosomal trafficking of integrins modulates these signals and contributes to cell motility and is required for cancer cell invasion. The phosphoprotein PEA-15 modulates integrin activation and ERK MAP Kinase signaling. To elucidate novel PEA-15 functions we utilized an unbiased proteomics approach. We identified several binding partners for PEA-15 in the endosome including clathrin and AP-2 as well as integrin β1 and other focal adhesion complex proteins. We confirmed these interactions using proximity ligation analysis, immunofluorescence imaging, pull-down and co-immunoprecipitation. We further found that PEA-15 is enriched in endosomes and was required for efficient endosomal internalization of α5β1 integrin and cellular migration. Importantly, PEA-15 promotion of migration was dependent on PEA-15 phosphorylation at serines 104 and 116. These data support a novel endosomal role for PEA-15 in control of endosomal trafficking of integrins through an association with the β1 integrin and clathrin complexes, and thereby regulation of cell motility.

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

BACKGROUND

For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.

RESULTS

Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.

CONCLUSIONS

From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression.

Phosphoproteomics Analysis Identifies Novel Candidate Substrates of the Nonreceptor Tyrosine Kinase, Src-related Kinase Lacking C-terminal Regulatory Tyrosine and N-terminal Myristoylation Sites (SRMS)

SRMS (Src-related kinase lacking C-terminal regulatory tyrosine and N-terminal myristoylation sites), also known as PTK 70 (Protein tyrosine kinase 70), is a non-receptor tyrosine kinase that belongs to the BRK family of kinases (BFKs). To date less is known about the cellular role of SRMS primarily because of the unidentified substrates or signaling intermediates regulated by the kinase. In this study, we used phosphotyrosine antibody-based immunoaffinity purification in large-scale label-free quantitative phosphoproteomics to identify novel candidate substrates of SRMS. Our analyses led to the identification of 1258 tyrosine-phosphorylated peptides which mapped to 663 phosphoproteins, exclusively from SRMS-expressing cells. DOK1, a previously characterized SRMS substrate, was also identified in our analyses. Functional enrichment analyses revealed that the candidate SRMS substrates were enriched in various biological processes including protein ubiquitination, mitotic cell cycle, energy metabolism and RNA processing, as well as Wnt and TNF signaling. Analyses of the sequence surrounding the phospho-sites in these proteins revealed novel candidate SRMS consensus substrate motifs. We utilized customized high-throughput peptide arrays to validate a subset of the candidate SRMS substrates identified in our MS-based analyses. Finally, we independently validated Vimentin and Sam68, as bona fide SRMS substrates through in vitro and in vivo assays. Overall, our study identified a number of novel and biologically relevant SRMS candidate substrates, which suggests the involvement of the kinase in a vast array of unexplored cellular functions.

T-Cell Exhaustion Signatures Vary with Tumor Type and Are Severe in Glioblastoma

Purpose: T-cell dysfunction is a hallmark of glioblastoma (GBM). Although anergy and tolerance have been well characterized, T-cell exhaustion remains relatively unexplored. Exhaustion, characterized in part by the upregulation of multiple immune checkpoints, is a known contributor to failures amid immune checkpoint blockade, a strategy that has lacked success thus far in GBM. This study is among the first to examine, and credential as bona fide, exhaustion among T cells infiltrating human and murine GBM.Experimental Design: Tumor-infiltrating and peripheral blood lymphocytes (TILs and PBLs) were isolated from patients with GBM. Levels of exhaustion-associated inhibitory receptors and poststimulation levels of the cytokines IFNγ, TNFα, and IL2 were assessed by flow cytometry. T-cell receptor Vβ chain expansion was also assessed in TILs and PBLs. Similar analysis was extended to TILs isolated from intracranial and subcutaneous immunocompetent murine models of glioma, breast, lung, and melanoma cancers.Results: Our data reveal that GBM elicits a particularly severe T-cell exhaustion signature among infiltrating T cells characterized by: (1) prominent upregulation of multiple immune checkpoints; (2) stereotyped T-cell transcriptional programs matching classical virus-induced exhaustion; and (3) notable T-cell hyporesponsiveness in tumor-specific T cells. Exhaustion signatures differ predictably with tumor identity, but remain stable across manipulated tumor locations.Conclusions: Distinct cancers possess similarly distinct mechanisms for exhausting T cells. The poor TIL function and severe exhaustion observed in GBM highlight the need to better understand this tumor-imposed mode of T-cell dysfunction in order to formulate effective immunotherapeutic strategies targeting GBM. Clin Cancer Res; 24(17); 4175-86. ©2018 AACRSee related commentary by Jackson and Lim, p. 4059.

Advances in mass spectrometry based strategies to study receptor tyrosine kinases

Receptor tyrosine kinases (RTKs) are key transmembrane environmental sensors that are capable of transmitting extracellular information into phenotypic responses, including cell proliferation, survival and metabolism. Advances in mass spectrometry (MS)-based phosphoproteomics have been instrumental in providing the foundations of much of our current understanding of RTK signalling networks and activation dynamics. Furthermore, new insights relating to the deregulation of RTKs in disease, for instance receptor co-activation and kinome reprogramming, have largely been identified using phosphoproteomic-based strategies. This review outlines the current approaches employed in phosphoproteomic workflows, including phosphopeptide enrichment and MS data-acquisition methods. Here, recent advances in the application of MS-based phosphoproteomics to bridge critical gaps in our knowledge of RTK signalling are focused on. The current limitations of the technology are discussed and emerging areas such as computational modelling, high-throughput phospho-proteomic workflows and next-generation single-cell approaches to further our understanding in new areas of RTK biology are highlighted.

Computational flow cytometry reveals that cryopreservation induces spermptosis but subpopulations of spermatozoa may experience capacitation-like changes

The reduced lifespan of cryopreserved spermatozoa in the mare reproductive tract has been attributed to both capacitative and apoptotic changes. However, there is a lack of studies investigating both phenomena simultaneously. In order to improve our knowledge in this particular point, we studied in raw and frozen-thawed samples apoptotic and capacitative markers using a wide battery of test based in flow cytometry. Apoptotic markers evaluated were caspase 3 activity, externalization of phosphatidylserine (PS), and mitochondrial membrane potential. Markers of changes resembling capacitation were membrane fluidity, tyrosine phosphorylation, and intracellular sodium. Conventional and computational flow cytometry using nonlinear dimensionally reduction techniques (t-distributed stochastic neighbor embedding (t-SNE)) and automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE) were used. Most of the changes induced by cryopreservation were apoptotic, with increase in caspase 3 activation (P < 0.01), PS translocation to the outer membrane (P < 0.001), loss of mitochondrial membrane potential (P < 0.05), and increase in intracellular Na+ (P < 0.01). Average values of markers of capacitative changes were not affected by cryopreservation; however, the analysis of the phenotype of individual spermatozoa using computational flow cytometry revealed the presence of subpopulations of spermatozoa experiencing capacitative changes. For the first time advanced computational techniques were applied to the analysis of spermatozoa, and these techniques were able to disclose relevant information of the ejaculate that remained hidden using conventional flow cytometry.

Neuroblastoma tyrosine kinase signaling networks involve FYN and LYN in endosomes and lipid rafts

Protein phosphorylation plays a central role in creating a highly dynamic network of interacting proteins that reads and responds to signals from growth factors in the cellular microenvironment. Cells of the neural crest employ multiple signaling mechanisms to control migration and differentiation during development. It is known that defects in these mechanisms cause neuroblastoma, but how multiple signaling pathways interact to govern cell behavior is unknown. In a phosphoproteomic study of neuroblastoma cell lines and cell fractions, including endosomes and detergent-resistant membranes, 1622 phosphorylated proteins were detected, including more than half of the receptor tyrosine kinases in the human genome. Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data. Clusters of proteins in these networks are indicative of functional signaling pathways. The analysis indicates that receptor tyrosine kinases are functionally compartmentalized into distinct collaborative groups distinguished by activation and intracellular localization of SRC-family kinases, especially FYN and LYN. Changes in intracellular localization of activated FYN and LYN were observed in response to stimulation of the receptor tyrosine kinases, ALK and KIT. The results suggest a mechanism to distinguish signaling responses to activation of different receptors, or combinations of receptors, that govern the behavior of the neural crest, which gives rise to neuroblastoma.

Neuroblastoma is a childhood cancer for which therapeutic progress has been slow. We analyzed a large number phosphorylated proteins in neuroblastoma cells to discern patterns that indicate functional signal transduction pathways. To analyze the data, we developed novel techniques that resolve data structure and visualize that structure as networks that represent both protein interactions and statistical relationships. We also fractionated neuroblastoma cells to examine the location of signaling proteins in different membrane fractions and organelles. The analysis revealed that signaling pathways are functionally and physically compartmentalized into distinct collaborative groups distinguished by phosphorylation patterns and intracellular localization. We found that two related proteins (FYN and LYN) act like central hubs in the tyrosine kinase signaling network that change intracellular localization and activity in response to activation of different receptors.

RCytoscape: tools for exploratory network analysis

BACKGROUND

Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.

RESULTS

RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.

CONCLUSIONS

Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations--molecular maps--created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.