CIN85 phosphorylation is essential for EGFR ubiquitination and sorting into multivesicular bodies

Ubiquitination of the epidermal growth factor receptor (EGFR) by cbl and its cognate adaptor cbl-interacting protein of 85 kDa (CIN85) is known to play an essential role in directing this receptor to the lysosome for degradation. The mechanisms by which this ubiquitin modification is regulated are not fully defined, nor is it clear where this process occurs. In this study we show that EGFR activation leads to a pronounced src-mediated tyrosine phosphorylation of CIN85 that subsequently influences EGFR ubiquitination. Of importance, phospho-CIN85 interacts with the Rab5-positive endosome, where it mediates the sequestration of the ubiquitinated receptor into multivesicular bodies (MVBs) for subsequent degradation. These findings provide novel insights into how src- kinase–based regulation of a cbl adaptor regulates the fate of the EGFR

Genome-wide Profiling of RNA splicing in prostate tumor from RNA-seq data using virtual microarrays

BACKGROUND: Second generation RNA sequencing technology (RNA-seq) offers the potential to interrogate genome-wide differential RNA splicing in cancer. However, since short RNA reads spanning spliced junctions cannot be mapped contiguously onto to the chromosomes, there is a need for methods to profile splicing from RNA-seq data. Before the invent of RNA-seq technologies, microarrays containing probe sequences representing exon-exon junctions of known genes have been used to hybridize cellular RNAs for measuring context-specific differential splicing. Here, we extend this approach to detect tumor-specific splicing in prostate cancer from a RNA-seq dataset. METHOD: A database, SPEventH, representing probe sequences of under a million non-redundant splice events in human is created with exon-exon junctions of optimized length for use as virtual microarray. SPEventH is used to map tens of millions of reads from matched tumor-normal samples from ten individuals with prostate cancer. Differential counts of reads mapped to each event from tumor and matched normal is used to identify statistically significant tumor-specific splice events in prostate. RESULTS: We find sixty-one (61) splice events that are differentially expressed with a p-value of less than 0.0001 and a fold change of greater than 1.5 in prostate tumor compared to the respective matched normal samples. Interestingly, the only evidence, EST (BF372485), in the public database for one of the tumor-specific splice event joining one of the intron in KLK3 gene to an intron in KLK2, is also derived from prostate tumor-tissue. Also, the 765 events with a p-value of less than 0.001 is shown to cluster all twenty samples in a context-specific fashion with few exceptions stemming from low coverage of samples. CONCLUSIONS: We demonstrate that virtual microarray experiments using a non-redundant database of splice events in human is both efficient and sensitive way to profile genome-wide splicing in biological samples and to detect tumor-specific splicing signatures in datasets from RNA-seq technologies. The signature from the large number of splice events that could cluster tumor and matched-normal samples into two tight separate clusters, suggests that differential splicing is yet another RNA phenotype, alongside gene expression and SNPs, that can be exploited for tumor stratification

Effects of CD2-associated protein deficiency on amyloid-β in neuroblastoma cells and in an APP transgenic mouse model

BACKGROUND: CD2-associated protein (CD2AP) is an SH3-containing scaffold adaptor protein which regulates the actin cytoskeleton. Recently, CD2AP was identified as a genetic risk factor for Alzheimer’s disease (AD) by several genome-wide association studies. One of the hallmarks of AD is the accumulation of aggregated forms of Amyloid-β (Aβ) in the brain. In humans, CD2AP AD susceptibility locus (rs9349407) is associated with an increased plaque burden. Aβ production is highly regulated by endocytosis and is influenced by lysosomal function. Lysosomal trafficking is influenced by CD2AP. In this study, we decreased CD2AP levels in N2a neuroblastoma cultures and PS1APP mice and analyzed Aβ levels and plaque burden. RESULTS: Our data show that suppressing CD2AP expression using shRNA in N2a-APP695 cells results in decreased cell membrane amyloid precursor protein, decreased Aβ release and a lower Aβ(42)/Aβ(40) ratio. CD2AP protein is expressed in the brain as detected by western blot, and the expression level is dependent on gene dosage. In 1-month old PS1APP mice, complete loss of CD2AP in brain resulted in a decreased Aβ(42)/Aβ(40) ratio in brain tissue lysates while there was no effect on Aβ deposition or accumulation in PS1APP mice expressing one copy of CD2AP. CONCLUSION: CD2-Associated Protein affects Aβ levels and Aβ(42)/Aβ(40) ratio in vitro. The effect of CD2-Associated Protein on Aβ metabolism is subtle in vivo

CD2AP Regulates The Migration of Plasmacytoid and Conventional Dendritic Cells and CD2AP Involvement in Amyloid Precursor Protein Trafficking is Linked to Neuronal Toxicity

CD2-associated protein (CD2AP) is implicated in diverse biological functions, including regulation of actin cytoskeleton dynamics and coordination of vesicular trafficking. Yet, despite its involvement in these processes, its precise function in vivo is still poorly understood. Recently, we observed that CD2AP is highly expressed in dendritic cells (DCs). DCs provide a critical link between the innate and adaptive immune system. Upon recognizing foreign antigen, DCs undergo a maturation program that results in the secretion of cytokines, and their subsequent migration to lymph nodes to present the antigen to naïve T cells. Both these processes are crucial steps in engaging the adaptive immune response and are dependent on proper vesicular trafficking and actin cytoskeleton remodeling. Therefore, we decided to examine whether CD2AP is required for either or both of these functions. We began our studies by first demonstrating that CD2AP is not required for the development of two major DC subsets - plasmacytoid DCs (pDCs) and conventional DCs (cDCs). In addition, we found no defects in the secretion of proinflammatory cytokines by both DC subsets, and no defects in type I IFN production, which is a major and defining function of pDCs. Since CD2AP is implicated in actin regulation, we studied migration of pDCs and cDCs to lymph nodes in inflammation/activation models. Surprisingly, we got very different results with the two DC subsets. The CD2AP knockout pDCs displayed dramatically reduced migration, while the cDCs showed enhanced migration. We are currently working out potential mechanisms to explain these intriguing results. Recent genome-wide association studies (GWAS) have implicated CD2AP as an Alzheimer\u27s disease susceptibility locus. Therefore, we sought to address whether the involvement of CD2AP in Alzheimer\u27s disease is at the level of regulating the trafficking of amyloid precursor protein (APP) and the production and/or secretion of beta-amyloid peptides. We determined that CD2AP deficiency in the context of APP overexpression in neuronal cells resulted in significant cell death. This defect correlated with decreased secretion and increased intracellular retention of beta-amyloid peptides. Beta-amyloid peptides aggregate at acidic pH and form cytotoxic fibrils. We are presently trying to determine if beta-amyloid peptides are either misdirected to or retained within a particular compartment/pathway. Our current studies point to CD2AP being involved in the endosomal recycling pathway

Integrative Analysis of Normal Long Intergenic Non-Coding RNAs in Prostate Cancer

<div><p>Recently, large numbers of normal human tissues have been profiled for non-coding RNAs and more than fourteen thousand long intergenic non-coding RNAs (lincRNAs) are found expressed in normal human tissues. The functional roles of these normal lincRNAs (nlincRNAs) in the regulation of protein coding genes in normal and disease biology are yet to be established. Here, we have profiled two RNA-seq datasets including cancer and matched non-neoplastic tissues from 12 individuals from diverse demography for both coding genes and nlincRNAs. We find 130 nlincRNAs significantly regulated in cancer, with 127 regulated in the same direction in the two datasets. Interestingly, according to Illumina Body Map, significant numbers of these nlincRNAs display baseline null expression in normal prostate tissues but are specific to other tissues such as thyroid, kidney, liver and testis. A number of the regulated nlincRNAs share loci with coding genes, which are either co-regulated or oppositely regulated in all cancer samples studied here. For example, in all cancer samples i) the nlincRNA, TCONS_00029157, and a neighboring tumor suppressor factor, SIK1, are both down regulated; ii) several thyroid-specific nlincRNAs in the neighborhood of the thyroid-specific gene TPO, are both up-regulated; and iii) the TCONS_00010581, an isoform of HEIH, is down-regulated while the neighboring EZH2 gene is up-regulated in cancer. Several nlincRNAs from a prostate cancer associated chromosomal locus, 8q24, are up-regulated in cancer along with other known prostate cancer associated genes including PCAT-1, PVT1, and PCAT-92. We observe that there is significant bias towards up-regulation of nlincRNAs with as high as 118 out of 127 up-regulated in cancer, even though regulation of coding genes is skewed towards down-regulation. Considering that all reported cancer associated lincRNAs (clincRNAs) are biased towards up-regulation, we conclude that this bias may be functionally relevant.</p></div

Interaction network of inactivated gene cluster from 17q21 locus using GeneMania.

<p>Interaction network of inactivated gene cluster from 17q21 locus using GeneMania.</p

A) Heatmap and B) dendrogram for all the lincRNAs along with their neighboring genes, which are also differentially regulated. Red arrows indicate clusters of neighboring gene and lincRNAs.

<p>A) Heatmap and B) dendrogram for all the lincRNAs along with their neighboring genes, which are also differentially regulated. Red arrows indicate clusters of neighboring gene and lincRNAs.</p

A) dendrogram and B) Heatmap of the 366 genes differentially regulated in cancer sample in both datasets.

<p>A) dendrogram and B) Heatmap of the 366 genes differentially regulated in cancer sample in both datasets.</p

Flowchart with converging significance of genes and nlincRNAs differentially regulated from the two datasets and the two methods.

<p>Flowchart with converging significance of genes and nlincRNAs differentially regulated from the two datasets and the two methods.</p