Anna M. Ritz

A New Approach for Quantitative Phosphoproteomic Dissection of Signaling Pathways Applied to T Cell Receptor Activation

Reversible protein phosphorylation plays a pivotal role in the regulation of cellular signaling pathways. Current approaches in phosphoproteomics focus on analysis of the global phosphoproteome in a single cellular state or of receptor stimulation time course experiments, often with a restricted number of time points. Although these studies have provided some insights into newly discovered phosphorylation sites that may be involved in pathways, they alone do not provide enough information to make precise predictions of the placement of individual phosphorylation events within a signaling pathway. Protein disruption and site-directed mutagenesis are essential to clearly define the precise biological roles of the hundreds of newly discovered phosphorylation sites uncovered in modern proteomics experiments. We have combined genetic analysis with quantitative proteomic methods and recently developed visual analysis tools to dissect the tyrosine phosphoproteome of isogenic Zap-70 tyrosine kinase null and reconstituted Jurkat T cells. In our approach, label-free quantitation using normalization to copurified phosphopeptide standards is applied to assemble high density temporal data within a single cell type, either Zap-70 null or reconstituted cells, providing a list of candidate phosphorylation sites that change in abundance after T cell stimulation. Stable isotopic labeling of amino acids in cell culture (SILAC) ratios are then used to compare Zap-70 null and reconstituted cells across a time course of receptor stimulation, providing direct information about the placement of newly observed phosphorylation sites relative to Zap-70. These methods are adaptable to any cell culture signaling system in which isogenic wild type and mutant cells have been or can be derived using any available phosphopeptide enrichment strategy.

Quantitative Time-Resolved Phosphoproteomic Analysis of Mast Cell Signaling

Mast cells play a central role in type I hypersensitivity reactions and allergic disorders such as anaphylaxis and asthma. Activation of mast cells, through a cascade of phosphorylation events, leads to the release of mediators of the early phase allergic response. Understanding the molecular architecture underlying mast cell signaling may provide possibilities for therapeutic intervention in asthma and other allergic diseases. Although many details of mast cell signaling have been described previously, a systematic, quantitative analysis of the global tyrosine phosphorylation events that are triggered by activation of the mast cell receptor is lacking. In many cases, the involvement of particular proteins in mast cell signaling has been established generally, but the precise molecular mechanism of the interaction between known signaling proteins often mediated through phosphorylation is still obscure. Using recently advanced methodologies in mass spectrometry, including automation of phosphopeptide enrichments and detection, we have now substantially characterized, with temporal resolution as short as 10 s, the sites and levels of tyrosine phosphorylation across 10 min of FcεRI-induced mast cell activation. These results reveal a far more extensive array of tyrosine phosphorylation events than previously known, including novel phosphorylation sites on canonical mast cell signaling molecules, as well as unexpected pathway components downstream of FcεRI activation. Furthermore, our results, for the first time in mast cells, reveal the sequence of phosphorylation events for 171 modification sites across 121 proteins in the MCP5 mouse mast cell line and 179 modification sites on 117 proteins in mouse bone marrow-derived mast cells.

Environmental chemistry through intelligent atmospheric data analysis

Here we present a new open-source software package designed to facilitate the analysis of atmospheric data, with emphasis on data mining applications applied to single-particle mass spectrometry data from aerosol particles. The software package, Enchilada (Environmental Chemistry through Intelligent Atmospheric Data Analysis), is designed to seamlessly handle large datasets, to allow for temporal aggregation of data from many instruments, and to integrate techniques such as clustering (K-means, K-medians, and Art-2a), labeling of peaks in mass spectra, and temporal correlations of multiple datasets from multiple instrument types. The software, which continues to be developed and improved, provides users with a single package to integrate data from multiple mass spectrometer systems (ATOFMS, PALMS, SPASS, Q-AMS) as well as any time-based data stream. A detailed description of the software and examples of analysis methods that are incorporated into it are described here.

Discovery of phosphorylation motif mixtures in phosphoproteomics data

MOTIVATION Modification of proteins via phosphorylation is a primary mechanism for signal transduction in cells. Phosphorylation sites on proteins are determined in part through particular patterns, or motifs, present in the amino acid sequence. RESULTS We describe an algorithm that simultaneously discovers multiple motifs in a set of peptides that were phosphorylated by several different kinases. Such sets of peptides are routinely produced in proteomics experiments.Our motif-finding algorithm uses the principle of minimum description length to determine a mixture of sequence motifs that distinguish a foreground set of phosphopeptides from a background set of unphosphorylated peptides. We show that our algorithm outperforms existing motif-finding algorithms on synthetic datasets consisting of mixtures of known phosphorylation sites. We also derive a motif specificity score that quantifies whether or not the phosphoproteins containing an instance of a motif have a significant number of known interactions. Application of our motif-finding algorithm to recently published human and mouse proteomic studies recovers several known phosphorylation motifs and reveals a number of novel motifs that are enriched for interactions with a particular kinase or phosphatase. Our tools provide a new approach for uncovering the sequence specificities of uncharacterized kinases or phosphatases.

Gremlin: An Interactive Visualization Model for Analyzing Genomic Rearrangements

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.

Detection of recurrent rearrangement breakpoints from copy number data

BackgroundCopy number variants (CNVs), including deletions, amplifications, and other rearrangements, are common in human and cancer genomes. Copy number data from array comparative genome hybridization (aCGH) and next-generation DNA sequencing is widely used to measure copy number variants. Comparison of copy number data from multiple individuals reveals recurrent variants. Typically, the interior of a recurrent CNV is examined for genes or other loci associated with a phenotype. However, in some cases, such as gene truncations and fusion genes, the target of variant lies at the boundary of the variant.ResultsWe introduce Neighborhood Breakpoint Conservation (NBC), an algorithm for identifying rearrangement breakpoints that are highly conserved at the same locus in multiple individuals. NBC detects recurrent breakpoints at varying levels of resolution, including breakpoints whose location is exactly conserved and breakpoints whose location varies within a gene. NBC also identifies pairs of recurrent breakpoints such as those that result from fusion genes. We apply NBC to aCGH data from 36 primary prostate tumors and identify 12 novel rearrangements, one of which is the well-known TMPRSS2-ERG fusion gene. We also apply NBC to 227 glioblastoma tumors and predict 93 novel rearrangements which we further classify as gene truncations, germline structural variants, and fusion genes. A number of these variants involve the protein phosphatase PTPN12 suggesting that deregulation of PTPN12, via a variety of rearrangements, is common in glioblastoma.ConclusionsWe demonstrate that NBC is useful for detection of recurrent breakpoints resulting from copy number variants or other structural variants, and in particular identifies recurrent breakpoints that result in gene truncations or fusion genes. Software is available at http://http.//cs.brown.edu/people/braphael/software.html.

Pathways on demand: automated reconstruction of human signaling networks

Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present PATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.

Quantitative Phosphoproteomics Reveals SLP-76 Dependent Regulation of PAG and Src Family Kinases in T Cells

The SH2-domain-containing leukocyte protein of 76 kDa (SLP-76) plays a critical scaffolding role in T cell receptor (TCR) signaling. As an adaptor protein that contains multiple protein-binding domains, SLP-76 interacts with many signaling molecules and links proximal receptor stimulation to downstream effectors. The function of SLP-76 in TCR signaling has been widely studied using the Jurkat human leukaemic T cell line through protein disruption or site-directed mutagenesis. However, a wide-scale characterization of SLP-76-dependant phosphorylation events is still lacking. Quantitative profiling of over a hundred tyrosine phosphorylation sites revealed new modes of regulation of phosphorylation of PAG, PI3K, and WASP while reconfirming previously established regulation of Itk, PLCγ, and Erk phosphorylation by SLP-76. The absence of SLP-76 also perturbed the phosphorylation of Src family kinases (SFKs) Lck and Fyn, and subsequently a large number of SFK-regulated signaling molecules. Altogether our data suggests unique modes of regulation of positive and negative feedback pathways in T cells by SLP-76, reconfirming its central role in the pathway.

Integrated genomics of ovarian xenograft tumor progression and chemotherapy response

BackgroundOvarian cancer is the most deadly gynecological cancer with a very poor prognosis. Xenograft mouse models have proven to be one very useful tool in testing candidate therapeutic agents and gene function in vivo. In this study we identify genes and gene networks important for the efficacy of a pre-clinical anti-tumor therapeutic, MT19c.MethodsIn order to understand how ovarian xenograft tumors may be growing and responding to anti-tumor therapeutics, we used genome-wide mRNA expression and DNA copy number measurements to identify key genes and pathways that may be critical for SKOV-3 xenograft tumor progression. We compared SKOV-3 xenografts treated with the ergocalciferol derived, MT19c, to untreated tumors collected at multiple time points. Cell viability assays were used to test the function of the PPARγ agonist, Rosiglitazone, on SKOV-3 cell growth.ResultsThese data indicate that a number of known survival and growth pathways including Notch signaling and general apoptosis factors are differentially expressed in treated vs. untreated xenografts. As tumors grow, cell cycle and DNA replication genes show increased expression, consistent with faster growth. The steroid nuclear receptor, PPARγ, was significantly up-regulated in MT19c treated xenografts. Surprisingly, stimulation of PPARγ with Rosiglitazone reduced the efficacy of MT19c and cisplatin suggesting that PPARγ is regulating a survival pathway in SKOV-3 cells. To identify which genes may be important for tumor growth and treatment response, we observed that MT19c down-regulates some high copy number genes and stimulates expression of some low copy number genes suggesting that these genes are particularly important for SKOV-3 xenograft growth and survival.ConclusionsWe have characterized the time dependent responses of ovarian xenograft tumors to the vitamin D analog, MT19c. Our results suggest that PPARγ promotes survival for some ovarian tumor cells. We propose that a combination of regulated expression and copy number can identify genes that are likely important for chemotherapy response. Our findings suggest a new approach to identify candidate genes that are critical for anti-tumor therapy.

Pathway analysis with signaling hypergraphs

Signaling pathways play an important role in the cells response to its environment. Signaling pathways are often represented as directed graphs, which are not adequate for modeling reactions such as complex assembly and dissociation, combinatorial regulation, and protein activation/inactivation. More accurate representations such as directed hypergraphs remain underutilized. In this paper, we present an extension of a directed hypergraph that we call a signaling hypergraph. We formulate a problem that asks what proteins and interactions must be involved in order to stimulate a specific response downstream of a signaling pathway. We relate this problem to computing the shortest acyclic B-hyperpath in a signaling hypergraph --- an NP-hard problem --- and present a mixed integer linear program to solve it. We demonstrate that the shortest hyperpaths computed in signaling hypergraphs are far more informative than shortest paths found in corresponding graph representations. Our results illustrate the potential of signaling hypergraphs as an improved representation of signaling pathways and motivate the development of novel hypergraph algorithms.