Objectives. To study whether the reinforced feedback in virtual environment (RFVE) is more effective than traditional rehabilitation (TR) for the treatment of upper limb motor function after stroke, regardless of stroke etiology (i.e., ischemic, hemorrhagic). Design. Randomized controlled trial. Participants. Forty-four patients affected by stroke. Intervention. The patients were randomized into two groups: RFVE (N = 23) and TR (N = 21), and stratified according to stroke etiology. The RFVE treatment consisted of multidirectional exercises providing augmented feedback provided by virtual reality, while in the TR treatment the same exercises were provided without augmented feedbacks. Outcome Measures. Fugl-Meyer upper extremity scale (F-M UE), Functional Independence Measure scale (FIM), and kinematics parameters (speed, time, and peak). Results. The F-M UE (P = 0.030), FIM (P = 0.021), time (P = 0.008), and peak (P = 0.018), were significantly higher in the RFVE group after treatment, but not speed (P = 0.140). The patients affected by hemorrhagic stroke significantly improved FIM (P = 0.031), time (P = 0.011), and peak (P = 0.020) after treatment, whereas the patients affected by ischemic stroke improved significantly only speed (P = 0.005) when treated by RFVE. Conclusion. These results indicated that some poststroke patients may benefit from RFVE program for the recovery of upper limb motor function. This trial is registered with NCT01955291.
Microarray analysis has been used to understand how gene regulation plays a critical role in neuronal injury, survival and repair following ischemic stroke. To identify the transcriptional regulatory elements responsible for ischemia-induced gene expression, we examined gene expression profiles of rat brains following focal ischemia and performed computational analysis of consensus transcription factor binding sites (TFBS) in the genes of the dataset. In this study, rats were sacrificed 24 h after middle cerebral artery occlusion (MCAO) stroke and gene transcription in brain tissues following ischemia/reperfusion was examined using Affymetrix GeneChip technology. The CONserved transcription FACtor binding site (CONFAC) software package was used to identify over-represented TFBS in the upstream promoter regions of ischemia-induced genes compared to control datasets. CONFAC identified 12 TFBS that were statistically over-represented from our dataset of ischemia-induced genes, including three members of the Ets-1 family of transcription factors (TFs). Microarray results showed that mRNA for Ets-1 was increased following tMCAO but not pMCAO. Immunohistochemical analysis of Ets-1 protein in rat brains following MCAO showed that Ets-1 was highly expressed in neurons in the brain of sham control animals. Ets-1 protein expression was virtually abolished in injured neurons of the ischemic brain but was unchanged in peri-infarct brain areas. These data indicate that TFs, including Ets-1, may influence neuronal injury following ischemia. These findings could provide important insights into the mechanisms that lead to brain injury and could provide avenues for the development of novel therapies.
Ischemia; Microarray; Reperfusion; Stroke; Transcription Factors; Rat
Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.
HIV-1 is inhibited early after entry into cells expressing some simian orthologues of the tripartite motif protein family member TRIM5α. Mutants of the human orthologue (TRIM5αhu) can also provide protection against HIV-1. The host protein cyclophilin A (CypA) binds incoming HIV-1 capsid (CA) proteins and enhances early stages of HIV-1 replication by unknown mechanisms. On the other hand, the CA-CypA interaction is known to increase HIV-1 susceptibility to restriction by TRIM5α. Previously, the mutation V86M in the CypA-binding loop of HIV-1 CA was found to be selected upon serial passaging of HIV-1 in cells expressing Rhesus macaque TRIM5α (TRIM5αrh). The objectives of this study were (i) to analyze whether V86M CA allows HIV-1 to escape mutants of TRIM5αhu, and (ii) to characterize the role of CypA in the resistance to TRIM5α conferred by V86M.
We find that in single-cycle HIV-1 vector transduction experiments, V86M confers partial resistance against R332G-R335G TRIM5αhu and other TRIM5αhu variable 1 region mutants previously isolated in mutagenic screens. However, V86M HIV-1 does not seem to be resistant to R332G-R335G TRIM5αhu in a spreading infection context. Strikingly, restriction of V86M HIV-1 vectors by TRIM5αhu mutants is mostly insensitive to the presence of CypA in infected cells. NMR experiments reveal that V86M alters CypA interactions with, and isomerisation of CA. On the other hand, V86M does not affect the CypA-mediated enhancement of HIV-1 replication in permissive human cells. Finally, qPCR experiments show that V86M increases HIV-1 transport to the nucleus of cells expressing restrictive TRIM5α.
Our study shows that V86M de-couples the two functions associated with CA-CypA binding, i.e. the enhancement of restriction by TRIM5α and the enhancement of HIV-1 replication in permissive human cells. V86M enhances the early stages of HIV-1 replication in restrictive cells by improving nuclear import. In summary, our data suggest that HIV-1 escapes restriction by TRIM5α through the selective disruption of CypA-dependent, TRIM5α-mediated inhibition of nuclear import. However, V86M does not seem to relieve restriction of a spreading HIV-1 infection by TRIM5αhu mutants, underscoring context-specific restriction mechanisms.
Anti-1-amino-3-[18F] fluorocyclobutane-1-carboxylic acid (anti-3-[18F] FACBC) is a synthetic amino acid positron emission tomography (PET) radiotracer with utility in the detection of recurrent prostate carcinoma. The aim of this study is to correlate uptake of anti-3-[18F] FACBC with histology of prostatectomy specimens in patients undergoing radical prostatectomy and to determine if uptake correlates to markers of tumor aggressiveness such as Gleason score. Ten patients with prostate carcinoma pre-radical prostatectomy underwent 45 minute dynamic PET-CT of the pelvis after IV injection of 347.8 ± 81.4 MBq anti-3-[18F] FACBC. Each prostate was co-registered to a separately acquired MR, divided into 12 sextants, and analyzed visually for abnormal focal uptake at 4, 16, 28, and 40 min post-injection by a single reader blinded to histology. SUVmax per sextant and total sextant activity (TSA) was also calculated. Histology and Gleason scores were similarly recorded by a urologic pathologist blinded to imaging. Imaging and histologic analysis were then compared. In addition, 3 representative sextants from each prostate were chosen based on highest, lowest and median SUVmax for immunohistochemical (IHC) analysis of Ki67, synaptophysin, P504s, chromogranin A, P53, androgen receptor, and prostein. 79 sextants had malignancy and 41 were benign. Highest combined sensitivity and specificity was at 28 min by visual analysis; 81.3% and 50.0% respectively. SUVmax was significantly higher (p<0.05) for malignant sextants (5.1±2.6 at 4 min; 4.5±1.6 at 16 min; 4.0±1.3 at 28 min; 3.8±1.0 at 40 min) compared to non-malignant sextants (4.0±1.9 at 4 min; 3.5±0.8 at 16 min; 3.4±0.9 at 28 min; 3.3±0.9 at 40 min), though there was overlap of activity between malignant and non-malignant sextants. SUVmax also significantly correlated (p<0.05) with Gleason score at all time points (r=0.28 at 4 min; r=0.42 at 16 min; r=0.46 at 28 min; r=0.48 at 40 min). There was no significant correlation of anti-3-[18F] FACBC SUVmax with Ki-67 or other IHC markers. Since there was no distinct separation between malignant and non-malignant sextants or between Gleason score levels, we believe that anti-3-[18F] FACBC PET should not be used alone for radiation therapy planning but may be useful to guide biopsy to the most aggressive lesion.
Positron emission tomography (PET); prostate carcinoma; anti-3-[18F] FACBC
Protein phosphatase 2A (PP2A) is an essential eukaryotic serine/threonine phosphatase known to play important roles in cell cycle regulation. Association of different B-type targeting subunits with the heterodimeric core (A/C) enzyme is known to be an important mechanism of regulating PP2A activity, substrate specificity, and localization. However, how the binding of these targeting subunits to the A/C heterodimer might be regulated is unknown. We have used the budding yeast Saccharomyces cerevisiae as a model system to investigate the hypothesis that covalent modification of the C subunit (Pph21p/Pph22p) carboxyl terminus modulates PP2A complex formation. Two approaches were taken. First, S. cerevisiae cells were generated whose survival depended on the expression of different carboxyl-terminal Pph21p mutants. Second, the major S. cerevisiae methyltransferase (Ppm1p) that catalyzes the methylation of the PP2A C subunit carboxyl-terminal leucine was identified, and cells deleted for this methyltransferase were utilized for our studies. Our results demonstrate that binding of the yeast B subunit, Cdc55p, to Pph21p was disrupted by either acidic substitution of potential carboxyl-terminal phosphorylation sites on Pph21p or by deletion of the gene for Ppm1p. Loss of Cdc55p association was accompanied in each case by a large reduction in binding of the yeast A subunit, Tpd3p, to Pph21p. Moreover, decreased Cdc55p and Tpd3p binding invariably resulted in nocodazole sensitivity, a known phenotype of CDC55 or TPD3 deletion. Furthermore, loss of methylation also greatly reduced the association of another yeast B-type subunit, Rts1p. Thus, methylation of Pph21p is important for formation of PP2A trimeric and dimeric complexes, and consequently, for PP2A function. Taken together, our results indicate that methylation and phosphorylation may be mechanisms by which the cell dynamically regulates PP2A complex formation and function.
Protein phosphatase 2A (PP2A) is a multifunctional serine/threonine phosphatase that is critical to many cellular processes including development, neuronal signaling, cell cycle regulation, and viral transformation. PP2A has been implicated in Ca2+-dependent signaling pathways, but how PP2A is targeted to these pathways is not understood. We have identified two calmodulin (CaM)-binding proteins that form stable complexes with the PP2A A/C heterodimer and may represent a novel family of PP2A B-type subunits. These two proteins, striatin and S/G2 nuclear autoantigen (SG2NA), are highly related WD40 repeat proteins of previously unknown function and distinct subcellular localizations. Striatin has been reported to associate with the postsynaptic densities of neurons, whereas SG2NA has been reported to be a nuclear protein expressed primarily during the S and G2 phases of the cell cycle. We show that SG2NA, like striatin, binds to CaM in a Ca2+-dependent manner. In addition to CaM and PP2A, several unidentified proteins stably associate with the striatin-PP2A and SG2NA-PP2A complexes. Thus, one mechanism of targeting and organizing PP2A with components of Ca2+-dependent signaling pathways may be through the molecular scaffolding proteins striatin and SG2NA.
Striatin and S/G2 nuclear autoantigen (SG2NA) are related proteins that contain membrane binding domains and associate with protein phosphatase 2A (PP2A) and many additional proteins that may be PP2A regulatory targets. Here we identify a major member of these complexes as class II mMOB1, a mammalian homolog of the yeast protein MOB1, and show that its phosphorylation appears to be regulated by PP2A. Yeast MOB1 is critical for cytoskeletal reorganization during cytokinesis and exit from mitosis. We show that mMOB1 associated with PP2A is not detectably phosphorylated in asynchronous murine fibroblasts. However, treatment with the PP2A inhibitor okadaic acid induces phosphorylation of PP2A-associated mMOB1 on serine. Moreover, specific inhibition of PP2A also results in hyperphosphorylation of striatin, SG2NA, and three unidentified proteins, suggesting that these proteins may also be regulated by PP2A. Indirect immunofluorescence produced highly similar staining patterns for striatin, SG2NA, and mMOB1, with the highest concentrations for each protein adjacent to the nuclear membrane. We also present evidence that these complexes may interact with each other. These data are consistent with a model in which PP2A may regulate mMOB1, striatin, and SG2NA to modulate changes in the cytoskeleton or interactions between the cytoskeleton and membrane structures.
Among American men, prostate cancer is the most common, non-cutaneous malignancy that accounted for an estimated 241,000 new cases and 34,000 deaths in 2011. Previous studies have suggested that Wnt pathway inhibitory genes are silenced by CpG hypermethylation, and other studies have suggested that genistein can demethylate hypermethylated DNA. Genistein is a soy isoflavone with diverse effects on cellular proliferation, survival, and gene expression that suggest it could be a potential therapeutic agent for prostate cancer. We undertook the present study to investigate the effects of genistein on the epigenome of prostate cancer cells and to discover novel combination approaches of other compounds with genistein that might be of translational utility. Here, we have investigated the effects of genistein on several prostate cancer cell lines, including the ARCaP-E/ARCaP-M model of the epithelial to mesenchymal transition (EMT), to analyze effects on their epigenetic state. In addition, we investigated the effects of combined treatment of genistein with the histone deacetylase inhibitor vorinostat on survival in prostate cancer cells.
Using whole genome expression profiling and whole genome methylation profiling, we have determined the genome-wide differences in genetic and epigenetic responses to genistein in prostate cancer cells before and after undergoing the EMT. Also, cells were treated with genistein, vorinostat, and combination treatment, where cell death and cell proliferation was determined.
Contrary to earlier reports, genistein did not have an effect on CpG methylation at 20 μM, but it did affect histone H3K9 acetylation and induced increased expression of histone acetyltransferase 1 (HAT1). In addition, genistein also had differential effects on survival and cooperated with the histone deacteylase inhibitor vorinostat to induce cell death and inhibit proliferation.
Our results suggest that there are a number of pathways that are affected with genistein and vorinostat treatment such as Wnt, TNF, G2/M DNA damage checkpoint, and androgen signaling pathways. In addition, genistein cooperates with vorinostat to induce cell death in prostate cancer cell lines with a greater effect on early stage prostate cancer.
Prostate cancer; Soy; Natural compounds; Epigenetics; Apoptosis
Histiocytoid cardiomyopathy (HC) is a rare but distinctive arrhythmogenic disorder characterized by incessant ventricular tachycardia, cardiomegaly, and often sudden death by age 2 years. The underlying genetic mechanism of HC has eluded researchers for decades. To reveal the molecular-genetic basis of HC, molecular analyses of HC hearts and hearts of age-matched controls were performed.
Total RNA and genomic DNA were prepared from formalin-fixed paraffin-embedded cardiac tissue from 12 cases of HC and 12 age-matched controls. To identify genes differentially expressed in HC, whole genome cDNA-mediated Annealing, Selection, extension and Ligation profiling was performed. TaqMan quantitative polymerase chain reaction confirmed changes in RNA expression. DNA copy number changes were measured by TaqMan copy number analysis.
Analysis of differential gene expression in HC cases identified two significantly down regulated gene sets aligned sequentially along the genome. The first gene cluster consisted of genes S100A8, S100A9, and S100A12 at 1q21.3c, and the second cluster consisted of genes IL1RL1 (ST2), IL18R1, and IL18RAP at 2q12.1a. Strong decreases in interleukin 33 expression were also observed. Decreases in copy number of the S100A genes were confirmed by TaqMan CNV assays. S100A genes are downstream of the p38-MAPK pathway that can be activated by interleukin 33 signaling.
These data suggest a model in which the interleukin 33-IL1RL1/p38-MAPK/S100A8-S100A9 axis is down regulated in HC cardiac tissue and provide several candidate genes on 1q21.3c and 2q12.1a for inherited mutations that may predispose individuals to HC.
histiocytoid cardiomyopathy; arrhythmia; SIDS; whole genome DASL
In this paper, we present a complete and novel workflow for quantitative nuclear feature analysis of glioblastoma using high-throughput whole-slide microscopy image processing as it relates to treatment response and patient survival. With a complete suite of computer algorithms, large numbers of micro-anatomical structures, in this case nuclei, are analyzed and represented efficiently from whole-slide digitized images with numerical features. With regard to endpoints of treatment response, the computerized analysis presents a better discrimination than traditional neuropathologic review. As a result, this analysis method shows potential to facilitate a better understanding of disease progression and patients’ response to therapy for glioblastoma.
Multi-modal, multi-scale data synthesis is becoming increasingly critical for successful translational biomedical research. In this paper, we present a large-scale investigative initiative on glioblastoma, a high-grade brain tumor, with complementary data types using in silico approaches. We integrate and analyze data from The Cancer Genome Atlas Project on glioblastoma that includes novel nuclear phenotypic data derived from microscopic slides, genotypic signatures described by transcriptional class and genetic alterations, and clinical outcomes defined by response to therapy and patient survival. Our preliminary results demonstrate numerous clinically and biologically significant correlations across multiple data types, revealing the power of in silico multi-modal data integration for cancer research.
Glioblastoma; multi-modal data process; in silico; cluster analysis; translational integration
The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.
Biology; brain tumor; image analysis; in silico; microscopy
Expression profiling studies using microarrays and other methods have shown that microRNAs (miRNAs) are dysregulated in a wide variety of human cancers. The up-regulation of miR-221 has been reported in carcinomas of the pancreas, breast, and papillary thyroid, as well as in glioblastoma and chronic lymphocytic leukaemia. In prostate cancer, however, down-regulation of miR-221 has been repeatedly confirmed in miRNA expression studies. Also unique to prostate cancer, and found in more than 50% of patients, is the aberrant expression of a known oncogene, the TMPRSS2:ERG fusion. To date, there has been no published study describing miRNA associations in prostate tumours that over-express the ERG oncogene from the TMPRSS2:ERG fusion transcript. Herein we report that in a large and diverse cohort of prostate carcinoma samples, miR-221 is down-regulated in patients with tumours bearing TMPRSS2:ERG fusion transcripts, thus providing a link between miRNA and gene fusion expression.
Prostate cancer; microRNA; Fusion gene; TMPRSS2:ERG
Background and objective
Morphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events.
Materials and methods
Methodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events.
Results and discussion
Analysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.
Digital pathology; computer-assisted image analysis; cell morphology; image cytometry; cancer; data management; data integration; RFID; temporal database; spatial database; glioma; glioblastomabrain tumor; emory; bioinformatics; transcription; genomics; microarray; biomedical informatics; imaging; high end computing; middleware; pathology
Recent studies suggest that cancer stem cells (CSCs) are responsible for cancer resistance to therapies. We therefore investigated how glioblastoma-derived CSCs respond to the treatment of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Neurospheres were generated from glioblastomas, characterized for CSC properties including self-renewal, cell differentiation and xenograft formation capacity, and analyzed for TRAIL-induced apoptosis, CASP8 genomic status, and caspase-8 protein expression. The neurosphere NSC326 was sensitive to TRAIL-induced apoptosis as evidenced by cell death and caspase-8, -3, and -7 enzymatic activities. In contrast, however, the neurosphere NSC189 was TRAIL-resistant. G-banding analysis identified five chromosomally distinguishable cell populations in the neurospheres. Fluorescence in situ hybridization revealed the variation of chromosome 2 copy number in these populations and the loss of CASP8 locus in 2q33-34 region in a small set of cell populations in the neurosphere. Immunohistochemistry of NSC189 cell blocks revealed the lack of caspase-8 protein in a subset of neurosphere cells. Western blotting and immunohistochemistry of human glioblastoma tumors demonstrated the expression of caspase-8 protein in the vast majority of the tumors as compared to normal human brain tissues that lack the caspase-8 expression. This study shows heterogeneity of glioblastomas and derived CSCs in the genomic status of CASP8, expression of caspase-8, and thus responsiveness to TRAIL-induced apoptosis. Clinic trials may consider genomic analysis of the cancer tissue to identify the genomic loss of CASP8 and use it as a genomic marker to predict the resistance of glioblastomas to TRAIL apoptosis pathway-targeted therapies.
Apoptosis; cancer stem cells; caspase-8; glioblastoma; TRAIL
In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas. This system integrates multiple modules designed for convenient human annotations, standard-based data management, efficient data query and analysis. In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 whole-slide images of glioma biopsies. After machine-based nuclei segmentation for whole-slide images, a set of features describing nuclear shape, texture and cytoplasmic staining is calculated to describe each nucleus. These features along with nuclear boundaries are represented by a standardized data model and saved in the spatial relational database in our framework. Features derived from nuclei classified by neuropathologists are retrieved from the database through efficient spatial queries and used to train distinct classifiers. The best average classification accuracy is 87.43% for 100 independent five-fold cross validations. This suggests that the derived nuclear and cytoplasmic features can achieve promising classification results for six nuclear classes commonly presented in gliomas. Our framework is generic, and can be easily adapted for other related applications.
Nuclei classification; feature selection; microscopy image analysis; metadata model; diffuse glioma
Large multimodal datasets such as The Cancer Genome Atlas present an opportunity to perform correlative studies of tissue morphology and genomics to explore the morphological phenotypes associated with gene expression and genetic alterations. In this paper we present an investigation of Cancer Genome Atlas data that correlates morphology with recently discovered molecular subtypes of glioblastoma. Using image analysis to segment and extract features from millions of cells, we calculate high-dimensional morphological signatures to describe trends of nuclear morphology and cytoplasmic staining in whole-slide images. We illustrate the similarities between the analysis of these signatures and predictive studies of gene expression, both in terms of limited sample size and high-dimensionality. Our top-down analysis demonstrates the power of morphological signatures to predict clinically-relevant molecular tumor subtypes, with 85.4% recognition of the proneural subtype. A complementary bottom-up analysis shows that self-aggregating clusters have statistically significant associations with tumor subtype and reveals the existence of remarkable structure in the morphological signature space of glioblastomas.
bioinformatics; in silico; digital pathology; image analysis; microscopy
SOX4 is a developmental transcription factor that is required for differentiation and proliferation in multiple tissues. SOX4 is overexpressed in many human malignancies, but the precise role of SOX4 in cancer progression is still not well understood. Thus, the identification of additional SOX4 binding partners is essential for elucidating the mechanism of SOX4-mediated effects in cancer progression.
Here, we have adapted a one-step affinity purification method that enables rapid purification of SOX4 complexes via intracellular biotinylation of the amino-terminus of SOX4 to perform large-scale proteomics analysis. We have discovered that junction plakoglobin (JUP) interacts with SOX4 in both the cytosol and the nucleus and the interaction between SOX4 and plakoglobin is significantly increased when prostate and breast cancer cells are stimulated with WNT3A. Interactions between SOX4 and plakoglobin were further enhanced by the nuclear export inhibitor leptomycin B (LMB), suggesting that plakoglobin promotes nuclear export of SOX4. The SOX4-plakoglobin complex affected the expression of Wnt pathway target genes and SOX4 downstream targets, such as AXIN2, DICER1, and DHX9. In addition, SOX4 DNA binding activity to the promoters of DICER1, AXIN2, DHX9 and SOX4 itself was reduced by conditions that promote SOX4-plakoglobin complex formation. Conditions that enhanced SOX4-plakoglobin interactions resulted in reduced transcriptional activity of β-catenin luciferase reporters.
These data suggest that this newly identified interaction between SOX4 and plakoglobin is inhibitory and provides new insights into the role of SOX4 in key pathways in cell proliferation, development, and cancer progression.
While numerous studies have implicated copy number variants (CNVs) in a range of neurological phenotypes, the impact relative to disease severity has been difficult to ascertain due to small sample sizes, lack of phenotypic details, and heterogeneity in platforms used for discovery. Using a customized microarray enriched for genomic hotspots, we assayed for large CNVs among 1,227 individuals with various neurological deficits including dyslexia (376), sporadic autism (350), and intellectual disability (ID) (501), as well as 337 controls. We show that the frequency of large CNVs (>1 Mbp) is significantly greater for ID–associated phenotypes compared to autism (p = 9.58×10−11, odds ratio = 4.59), dyslexia (p = 3.81×10−18, odds ratio = 14.45), or controls (p = 2.75×10−17, odds ratio = 13.71). There is a striking difference in the frequency of rare CNVs (>50 kbp) in autism (10%, p = 2.4×10−6, odds ratio = 6) or ID (16%, p = 3.55×10−12, odds ratio = 10) compared to dyslexia (2%) with essentially no difference in large CNV burden among dyslexia patients compared to controls. Rare CNVs were more likely to arise de novo (64%) in ID when compared to autism (40%) or dyslexia (0%). We observed a significantly increased large CNV burden in individuals with ID and multiple congenital anomalies (MCA) compared to ID alone (p = 0.001, odds ratio = 2.54). Our data suggest that large CNV burden positively correlates with the severity of childhood disability: ID with MCA being most severely affected and dyslexics being indistinguishable from controls. When autism without ID was considered separately, the increase in CNV burden was modest compared to controls (p = 0.07, odds ratio = 2.33).
Deletions and duplications, termed copy number variants (CNVs), have been implicated in a variety of neurodevelopmental disorders including intellectual disability (ID), autism, and schizophrenia. Our understanding of the relevance of large, rare CNVs in a range of neurodevelopmental phenotypes, varying in severity and prevalence, has been difficult because these studies were restricted to the analysis of one disorder at a time using different CNV detection platforms, insufficient sample sizes, and a lack of detailed clinical information. We tested 1,227 individuals with different neurological diseases including dyslexia, autism, and ID using the same CNV detection platform. We observed striking differences in CNV burden and inheritance characteristics among these cohorts and show that ID is the primary correlate of large CNV burden. This correlation is well illustrated by a comparison of autism patients with and without ID—where the latter show only modest increases in large CNV burden compared to controls. We also find significant depletion in the frequency of large CNVs in dyslexia compared to the other cohorts. Further studies on larger sets of individuals using high-resolution arrays and next-generation sequencing are warranted for a detailed understanding of the relative contribution of genetic variants to neurodevelopmental disorders.
In biomedical studies, it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical variables are modeled nonlinearly using penalized regression splines. We show through simulation studies that our model has better operating characteristics compared to several existing models. In particular, we show that there is a non-negligible effect on prediction as well as feature selection when nonlinear clinical effects are misspecified as linear. This work is motivated by a recent prostate cancer study, where investigators collected gene expression data along with established prognostic clinical variables and the primary endpoint is time to prostate cancer recurrence. We analyzed the prostate cancer data and evaluated prediction performance of several models based on the extended c statistic for censored data, showing that 1) the relationship between the clinical variable, prostate specific antigen, and the prostate cancer recurrence is likely nonlinear, i.e., the time to recurrence decreases as PSA increases and it starts to level off when PSA becomes greater than 11; 2) correct specification of this nonlinear effect improves performance in prediction and feature selection; and 3) addition of gene expression data does not seem to further improve the performance of the resultant risk prediction scores.
Accelerated Failure Time Model; Feature Selection; Lasso; Partly Linear Model; Penalized Splines; Rank Estimation; Risk Prediction
Striatin, a putative protein phosphatase 2A (PP2A) B-type regulatory subunit, is a multi-domain scaffolding protein that has recently been linked to several diseases including cerebral cavernous malformation (CCM), which causes symptoms ranging from headaches to stroke. Striatin association with the PP2A A/C (structural subunit/catalytic subunit) heterodimer alters PP2A substrate specificity, but targets and roles of striatin-associated PP2A are not known. In addition to binding the PP2A A/C heterodimer to form a PP2A holoenzyme, striatin associates with cerebral cavernous malformation 3 (CCM3) protein, the mammalian Mps one binder (MOB) homolog, Mob3/phocein, the mammalian sterile 20-like (Mst) kinases, Mst3, Mst4 and STK25, and several other proteins to form a large signaling complex. Little is known about the molecular architecture of the striatin complex and the regulation of these sterile 20-like kinases.
To help define the molecular organization of striatin complexes and to determine whether Mst3 might be negatively regulated by striatin-associated PP2A, a structure-function analysis of striatin was performed. Two distinct regions of striatin are capable of stably binding directly or indirectly to Mob3--one N-terminal, including the coiled-coil domain, and another more C-terminal, including the WD-repeat domain. In addition, striatin residues 191-344 contain determinants necessary for efficient association of Mst3, Mst4, and CCM3. PP2A associates with the coiled-coil domain of striatin, but unlike Mob3 and Mst3, its binding appears to require striatin oligomerization. Deletion of the caveolin-binding domain on striatin abolishes striatin family oligomerization and PP2A binding. Point mutations in striatin that disrupt PP2A association cause hyperphosphorylation and activation of striatin-associated Mst3.
Striatin orchestrates the regulation of Mst3 by PP2A. It binds Mst3 likely as a dimer with CCM3 via residues lying between striatin's calmodulin-binding and WD-domains and recruits the PP2A A/C heterodimer to its coiled-coil/oligomerization domain. Residues outside the previously reported coiled-coil domain of striatin are necessary for its oligomerization. Striatin-associated PP2A is critical for Mst3 dephosphorylation and inactivation. Upon inhibition of PP2A, Mst3 activation appears to involve autophosphorylation of multiple activation loop phosphorylation sites. Mob3 can associate with striatin sequences C-terminal to the Mst3 binding site but also with sequences proximal to striatin-associated PP2A, consistent with a possible role for Mob 3 in the regulation of Mst3 by PP2A.
Silencing of individual genes can occur by genetic and epigenetic processes during carcinogenesis, but the underlying mechanisms remain unclear. By creating an integrated prostate cancer epigenome map using tiling arrays, we show that contiguous regions of gene suppression commonly occur due to Long Range Epigenetic Silencing (LRES). We identified 47 novel LRES regions in prostate cancer, typically spanning ~2 Mb and harbouring ~12 genes, with a prevalence of tumour suppressor genes and miRNAs. Our data reveal that LRES is associated with regional histone deacetylation combined with sub-domains of different epigenetic remodelling patterns, that include re-enforcement, gain or exchange of repressive histone and DNA methylation marks. The transcriptional and epigenetic state of genes in normal prostate epithelial and human embryonic stem cells can play a critical role in defining the mode of cancer-associated epigenetic remodelling. We propose that a consolidation or effective reduction of the cancer genome commonly occurs in domains, due to a combination of LRES and LOH or genomic deletion, resulting in reduced transcriptional plasticity within these regions.
Chromatin/Epigenetic regulation; Cancer; Transcription
Hypoxia inducible factor-1 (HIF-1) is the central mediator of the cellular response to low oxygen and functions as a transcription factor for a broad range of genes that provide adaptive responses to oxygen deprivation. HIF-1 is over-expressed in cancer and has become an important therapeutic target in solid tumors. In this study, a novel HIF-1α inhibitor was identified and its molecular mechanism was investigated.
Using a HIF-responsive reporter cell-based assay, a 10,000-membered natural product-like chemical compound library was screened to identify novel HIF-1 inhibitors. This led us to discover KC7F2, a lead compound with a central structure of cystamine. The effects of KC7F2 on HIF-1 transcription, translation and protein degradation processes were analyzed.
KC7F2 markedly inhibited HIF-mediated transcription in cells derived from different tumor types, including glioma, breast and prostate cancers and exhibited enhanced cytotoxicity under hypoxia. KC7F2 prevented the activation of HIF-target genes such as Carbonic Anhydrase IX, Matrix Metalloproteinase 2 (MMP2), Endothelin 1 and Enolase 1. Investigation of the mechanism of action of KC7F2 showed that it worked through the down-regulation of HIF-1α protein synthesis, an effect accompanied by the suppression of the phosphorylation of eukaryotic translation initiation factor 4E binding protein 1 (4EBP1) and p70 S6 kinase (S6K), key regulators of HIF-1α protein synthesis.
These results show that KC7F2 is a potent HIF-1 pathway inhibitor and that its potential as a cancer therapy agent warrants further study.
The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium's expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy.