Evaluation of Protein Profiles From Treated Xenograft Tumor Models Identifies an Antibody Panel for Formalin-fixed and Paraffin-embedded (FFPE) Tissue Analysis by Reverse Phase Protein Arrays (RPPA)

Reverse phase protein arrays (RPPA) are an established tool for measuring the expression and activation status of multiple proteins in parallel using only very small amounts of tissue. Several studies have demonstrated the value of this technique for signaling pathway analysis using proteins extracted from fresh frozen (FF) tissue in line with validated antibodies for this tissue type; however, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation in the clinical setting. Hence, we performed RPPA to measure profiles for a set of 300 protein markers using matched FF and FFPE tissue specimens to identify which markers performed similarly using the RPPA technique in fixed and unfixed tissues. Protein lysates were prepared from matched FF and FFPE tissue specimens of individual tumors taken from three different xenograft models of human cancer. Materials from both untreated mice and mice treated with either anti-HER3 or bispecific anti-IGF-1R/EGFR monoclonal antibodies were analyzed. Correlations between signals from FF and FFPE tissue samples were investigated. Overall, 60 markers were identified that produced comparable profiles between FF and FFPE tissues, demonstrating significant correlation between the two sample types. The top 25 markers also showed significance after correction for multiple testing. The panel of markers covered several clinically relevant tumor signaling pathways and both phosphorylated and nonphosphorylated proteins were represented. Biologically relevant changes in marker expression were noted when RPPA profiles from treated and untreated xenografts were compared. These data demonstrate that, using appropriately selected antibodies, RPPA analysis from FFPE tissue is well feasible and generates biologically meaningful information. The identified panel of markers that generate similar profiles in matched fixed and unfixed tissue samples may be clinically useful for pharmacodynamic studies of drug effect using FFPE tissues.Many human diseases are characterized by abnormalities in complex signaling pathways (1). The expression and activation status of proteins from these deregulated pathways has traditionally been analyzed using single marker techniques such as immunohistochemistry and Western blotting. Although these techniques have provided valuable information on the molecular abnormalities underlying human disease, they are labor intensive, have a low throughput, and often require high sample volume. Furthermore, techniques such as Western blotting are not applicable in the routine clinical setting. Miniaturized parallel immunoassay techniques have been developed in recent years and have played a pivotal role in biomarker discovery (2). Antibody arrays enable multiple potential disease markers to be investigated in a single sample in parallel (3). Beyond this, Reverse Phase Protein Arrays (RPPA)¹ are sensitive high throughput tools that can quantify protein expression levels and activation status (posttranslational modifications such as phosphorylation) in multiple experimental samples simultaneously. The technique requires only minute amounts of samples, printed as lysate arrays onto slides, and hundreds of markers of interest can be investigated, array by array, in a miniaturized dot blot manner. Numerous reports have demonstrated that RPPA can be applied to various sources of cells and tissues to analyze protein profiles, signaling pathway networks, and for the identification of biomarkers (4–13). A recently published workshop report reviews the full potential and advances of RPPA for use in clinical, translational, and basic research (11).In oncology, the parallel profiling of multiple protein markers is particularly desirable to study tumor initiation and progression, to classify tumor disease states on the molecular level, and to discover and monitor biomarkers that can predict therapeutic response or tumor recurrence (14–16). The study of signaling response and analysis of pharmacodynamic (PD) markers upon treatment using in vitro and in vivo test systems (e.g. cell line or patient derived xenograft tumor models) is an established component of preclinical and early clinical drug development. These techniques can provide evidence of target pathway modulation for new therapeutic lead candidate compounds and provide valuable information on the drug mode of action (17), especially in the translational phase. Multiplex analyses of PD biomarkers by RPPA have been performed in vitro using cancer cell lines (18, 19) as well as in patient-derived tumor tissue and blood samples (20, 21) to assess response to treatment and target inhibition. A combination of RPPA signaling pathway mapping and functional PET imaging has recently been successfully evaluated in xenograft models as an early response PD marker for anti-cancer drug efficacy (13).Translating miniaturized multiple protein analysis platforms-such as RPPA - from preclinical to clinical applicability is highly desirable; however, issues such as the limited amount of available clinical samples and tumor heterogeneity must first be addressed. Furthermore, most studies of RPPA in tumor tissue to date have been conducted using proteins extracted from fresh-frozen (FF) tissue specimens; whereas, formalin fixation and paraffin embedding (FFPE) is the standard method for tissue preservation used in clinical pathology laboratories. FFPE yields excellent tissue architecture for histological assessment and enables analysis of individual proteins in situ by techniques such as immunohistochemistry. However, formalin fixation leads to extensive protein–protein and protein–nucleic acid cross-linking (22), which can hamper protein extraction and reduce both the overall yield of extracted protein and the profile of proteins detectable by proteomic techniques (23, 24). Furthermore, formalin-induced cross-linking induces conformational changes in protein structure that can alter the immunoreactivity of some proteins in situ by hiding or altering peptide epitopes (25, 26). Such artifacts are absent from snap-frozen tissue; therefore, protein profiles obtained from FF tissue are likely to reflect the in vivo biology of the tumor more closely. However, FF tumor tissue is not widely available because it is costly to collect and maintain in the clinical setting. FFPE tissue samples are routinely archived by nearly every hospital and offer a unique opportunity to study thousands of samples retrospectively with extensive clinical records and follow-up information.Several groups have now established protocols for retrieving cross-linked proteins from fixed tissues (27–33). These methods are mainly based on the use of concentrated ionic detergents and high temperature protocols closely related to the antigen retrieval methods developed for immunohistochemistry. These studies show that obtaining nondegraded, full-length proteins from FFPE tissues for multiplex analyses is feasible (27–33). More recently, protein extraction techniques optimized for fixed samples have been used to successfully conduct RPPA using FFPE tissue biopsies from different cancer types (34–40). Guo et al. systematically investigated several protein extraction methods and demonstrated that RPPA of FFPE materials is feasible, reproducible and can generate biologically relevant protein profiles (41). Other studies have confirmed the validity of this approach and shown that data generated from RPPA analyses of FFPE tissue demonstrate good concordance with traditional immunohistochemistry markers such as HER2 protein in breast cancer (34, 40). However, to date, analyses have been performed only for a limited set of protein markers.To evaluate whether analysis of a broader panel of protein markers is feasible and generates meaningful data from FFPE tumor tissue sections, we conducted RPPA on matched samples of FF and FFPE tissues using a set of 300 markers, the largest panel reported to date. Our aim was to identify markers that performed similarly when comparing the protein profiles measured in protein extracts from matched FF and FFPE tissue, using RPPA assays established for use in frozen tissues. Correlating selected markers and assays in such a way should qualify RPPA for further use with FFPE tissues of clinical relevance, e.g. in PD marker studies. In this paper, we have specifically focused on the technical issues relevant for using the RPPA platform in a clinical setting, and did not address the biology of the test systems used in detail. However, the models used have been pre-characterized to identify key signaling parameters in context of targeted drug treatment (42). We conducted a systematic comparison of RPPA protein profiles in matched FF and FFPE tumor tissues resected from three different xenograft models of human cancer, each treated with targeted therapeutic antibodies that have previously been shown to achieve tumor growth inhibition. Furthermore, we investigated the effect of targeted drug treatment on protein expression and activation status, and the concordance of matched FF and FFPE tissue RPPA profiles. Finally, with one of the applied tumor models, we compared a set of protein profiles measured with two different multiple assay platforms - the RPPA and the Luminex Bio-Plex system, and discuss their relevance with respect to the analysis of FFPE tissue.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Printor, a Novel TorsinA-interacting Protein Implicated in Dystonia Pathogenesis

Early onset generalized dystonia (DYT1) is an autosomal dominant neurological disorder caused by deletion of a single glutamate residue (torsinA ΔE) in the C-terminal region of the AAA⁺ (ATPases associated with a variety of cellular activities) protein torsinA. The pathogenic mechanism by which torsinA ΔE mutation leads to dystonia remains unknown. Here we report the identification and characterization of a 628-amino acid novel protein, printor, that interacts with torsinA. Printor co-distributes with torsinA in multiple brain regions and co-localizes with torsinA in the endoplasmic reticulum. Interestingly, printor selectively binds to the ATP-free form but not to the ATP-bound form of torsinA, supporting a role for printor as a cofactor rather than a substrate of torsinA. The interaction of printor with torsinA is completely abolished by the dystonia-associated torsinA ΔE mutation. Our findings suggest that printor is a new component of the DYT1 pathogenic pathway and provide a potential molecular target for therapeutic intervention in dystonia.Early onset generalized torsion dystonia (DYT1) is the most common and severe form of hereditary dystonia, a movement disorder characterized by involuntary movements and sustained muscle spasms (1). This autosomal dominant disease has childhood onset and its dystonic symptoms are thought to result from neuronal dysfunction rather than neurodegeneration (2, 3). Most DYT1 cases are caused by deletion of a single glutamate residue at positions 302 or 303 (torsinA ΔE) of the 332-amino acid protein torsinA (4). In addition, a different torsinA mutation that deletes amino acids Phe³²³–Tyr³²⁸ (torsinA Δ323–328) was identified in a single family with dystonia (5), although the pathogenic significance of this torsinA mutation is unclear because these patients contain a concomitant mutation in another dystonia-related protein, ϵ-sarcoglycan (6). Recently, genetic association studies have implicated polymorphisms in the torsinA gene as a genetic risk factor in the development of adult-onset idiopathic dystonia (7, 8).TorsinA contains an N-terminal endoplasmic reticulum (ER)³ signal sequence and a 20-amino acid hydrophobic region followed by a conserved AAA⁺ (ATPases associated with a variety of cellular activities) domain (9, 10). Because members of the AAA⁺ family are known to facilitate conformational changes in target proteins (11, 12), it has been proposed that torsinA may function as a molecular chaperone (13, 14). TorsinA is widely expressed in brain and multiple other tissues (15) and is primarily associated with the ER and nuclear envelope (NE) compartments in cells (16–20). TorsinA is believed to mainly reside in the lumen of the ER and NE (17–19) and has been shown to bind lamina-associated polypeptide 1 (LAP1) (21), lumenal domain-like LAP1 (LULL1) (21), and nesprins (22). In addition, recent evidence indicates that a significant pool of torsinA exhibits a topology in which the AAA⁺ domain faces the cytoplasm (20). In support of this topology, torsinA is found in the cytoplasm, neuronal processes, and synaptic terminals (2, 3, 15, 23–26) and has been shown to bind cytosolic proteins snapin (27) and kinesin light chain 1 (20). TorsinA has been proposed to play a role in several cellular processes, including dopaminergic neurotransmission (28–31), NE organization and dynamics (17, 22, 32), and protein trafficking (27, 33). However, the precise biological function of torsinA and its regulation remain unknown.To gain insights into torsinA function, we performed yeast two-hybrid screens to search for torsinA-interacting proteins in the brain. We report here the isolation and characterization of a novel protein named printor (protein interactor of torsinA) that interacts selectively with wild-type (WT) torsinA but not the dystonia-associated torsinA ΔE mutant. Our data suggest that printor may serve as a cofactor of torsinA and provide a new molecular target for understanding and treating dystonia.     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]