Paul M. Krzyzanowski

Loss of Tankyrase-Mediated Destruction of 3BP2 Is the Underlying Pathogenic Mechanism of Cherubism

Cherubism is an autosomal-dominant syndrome characterized by inflammatory destructive bony lesions resulting in symmetrical deformities of the facial bones. Cherubism is caused by mutations in Sh3bp2, the gene that encodes the adaptor protein 3BP2. Most identified mutations in 3BP2 lie within the peptide sequence RSPPDG. A mouse model of cherubism develops hyperactive bone-remodeling osteoclasts and systemic inflammation characterized by expansion of the myelomonocytic lineage. The mechanism by which cherubism mutations alter 3BP2 function has remained obscure. Here we show that Tankyrase, a member of the poly(ADP-ribose)polymerase (PARP) family, regulates 3BP2 stability through ADP-ribosylation and subsequent ubiquitylation by the E3-ubiquitin ligase RNF146 in osteoclasts. Cherubism mutations uncouple 3BP2 from Tankyrase-mediated protein destruction, which results in its stabilization and subsequent hyperactivation of the SRC, SYK, and VAV signaling pathways.

Study of stem cell function using microarray experiments

DNA Microarrays are used to simultaneously measure the levels of thousands of mRNAs in a sample. We illustrate here that a collection of such measurements in different cell types and states is a sound source of functional predictions, provided the microarray experiments are analogous and the cell samples are appropriately diverse. We have used this approach to study stem cells, whose identity and mechanisms of control are not well understood, generating Affymetrix microarray data from more than 200 samples, including stem cells and their derivatives, from human and mouse. The data can be accessed online (StemBase; http://www.scgp.ca:8080/StemBase/).

CaPSID: A bioinformatics platform for computational pathogen sequence identification in human genomes and transcriptomes

BackgroundIt is now well established that nearly 20% of human cancers are caused by infectious agents, and the list of human oncogenic pathogens will grow in the future for a variety of cancer types. Whole tumor transcriptome and genome sequencing by next-generation sequencing technologies presents an unparalleled opportunity for pathogen detection and discovery in human tissues but requires development of new genome-wide bioinformatics tools.ResultsHere we present CaPSID (Computational Pathogen Sequence IDentification), a comprehensive bioinformatics platform for identifying, querying and visualizing both exogenous and endogenous pathogen nucleotide sequences in tumor genomes and transcriptomes. CaPSID includes a scalable, high performance database for data storage and a web application that integrates the genome browser JBrowse. CaPSID also provides useful metrics for sequence analysis of pre-aligned BAM files, such as gene and genome coverage, and is optimized to run efficiently on multiprocessor computers with low memory usage.ConclusionsTo demonstrate the usefulness and efficiency of CaPSID, we carried out a comprehensive analysis of both a simulated dataset and transcriptome samples from ovarian cancer. CaPSID correctly identified all of the human and pathogen sequences in the simulated dataset, while in the ovarian dataset CaPSID’s predictions were successfully validated in vitro.

StemBase: a resource for the analysis of stem cell gene expression data.

StemBase is a database of gene expression data obtained from stem cells and derivatives mainly from mouse and human using DNA microarrays and Serial Analysis of Gene Expression. Here, we describe this database and indicate ways to use it for the study the expression of particular genes in stem cells or to search for genes with particular expression profiles in stem cells, which could be associated to stem cell function or used as stem cell markers.

Computational approaches to discovering noncoding RNA.

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA‐related material is leading the field toward increased use of such high‐throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs. WIREs RNA 2012, 3:567–579. doi: 10.1002/wrna.1121

Recent developments in StemBase: a tool to study gene expression in human and murine stem cells

BackgroundCurrently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation.FindingsSince its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments.ConclusionStemBase can be used to study gene expression in human and murine stem cells and is available at http://www.stembase.ca.

Simple multiplexed PCR-based barcoding of DNA for ultrasensitive mutation detection by next-generation sequencing

Detection of extremely rare variant alleles within a complex mixture of DNA molecules is becoming increasingly relevant in many areas of clinical and basic research, such as the detection of circulating tumor DNA in the plasma of cancer patients. Barcoding of DNA template molecules early in next-generation sequencing (NGS) library construction provides a way to identify and bioinformatically remove polymerase errors that otherwise make detection of these rare variants very difficult. Several barcoding strategies have been reported, but all require long and complex library preparation protocols. Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing (SiMSen-seq) was developed to generate targeted barcoded libraries with minimal DNA input, flexible target selection and a very simple, short (∼4 h) library construction protocol. The protocol comprises a three-cycle barcoding PCR step followed directly by adaptor PCR to generate the library and then bead purification before sequencing. Thus, SiMSen-seq allows detection of variant alleles at <0.1% frequency with easy customization of library content (from 1 to 40+ PCR amplicons) and a protocol that can be implemented in any molecular biology laboratory. Here, we provide a detailed protocol for assay development and describe software to process the barcoded sequence reads.

Genomics-Driven Precision Medicine for Advanced Pancreatic Cancer: Early Results from the COMPASS Trial.

Purpose: To perform real-time whole genome sequencing (WGS) and RNA sequencing (RNASeq) of advanced pancreatic ductal adenocarcinoma (PDAC) to identify predictive mutational and transcriptional features for better treatment selection. Experimental Design: Patients with advanced PDAC were prospectively recruited prior to first-line combination chemotherapy. Fresh tumor tissue was acquired by image-guided percutaneous core biopsy for WGS and RNASeq. Laser capture microdissection was performed for all cases. Primary endpoint was feasibility to report WGS results prior to first disease assessment CT scan at 8 weeks. The main secondary endpoint was discovery of patient subsets with predictive mutational and transcriptional signatures. Results: Sixty-three patients underwent a tumor biopsy between December 2015 and June 2017. WGS and RNASeq were successful in 62 (98%) and 60 (95%), respectively. Genomic results were reported at a median of 35 days (range, 19–52 days) from biopsy, meeting the primary feasibility endpoint. Objective responses to first-line chemotherapy were significantly better in patients with the classical PDAC RNA subtype compared with those with the basal-like subtype (P = 0.004). The best progression-free survival was observed in those with classical subtype treated with m-FOLFIRINOX. GATA6 expression in tumor measured by RNA in situ hybridization was found to be a robust surrogate biomarker for differentiating classical and basal-like PDAC subtypes. Potentially actionable genetic alterations were found in 30% of patients. Conclusions: Prospective genomic profiling of advanced PDAC is feasible, and our early data indicate that chemotherapy response differs among patients with different genomic/transcriptomic subtypes. Clin Cancer Res; 24(6); 1344–54. ©2017 AACR.

Identification of novel stem cell markers using gap analysis of gene expression data

We describe a method for detecting marker genes in large heterogeneous collections of gene expression data. Markers are identified and characterized by the existence of demarcations in their expression values across the whole dataset, which suggest the presence of groupings of samples. We apply this method to DNA microarray data generated from 83 mouse stem cell related samples and describe 426 selected markers associated with differentiation to establish principles of stem cell evolution.

Integration of expressed sequence tag data flanking predicted RNA secondary structures facilitates novel non-coding RNA discovery.

Many computational methods have been used to predict novel non-coding RNAs (ncRNAs), but none, to our knowledge, have explicitly investigated the impact of integrating existing cDNA-based Expressed Sequence Tag (EST) data that flank structural RNA predictions. To determine whether flanking EST data can assist in microRNA (miRNA) prediction, we identified genomic sites encoding putative miRNAs by combining functional RNA predictions with flanking ESTs data in a model consistent with miRNAs undergoing cleavage during maturation. In both human and mouse genomes, we observed that the inclusion of flanking ESTs adjacent to and not overlapping predicted miRNAs significantly improved the performance of various methods of miRNA prediction, including direct high-throughput sequencing of small RNA libraries. We analyzed the expression of hundreds of miRNAs predicted to be expressed during myogenic differentiation using a customized microarray and identified several known and predicted myogenic miRNA hairpins. Our results indicate that integrating ESTs flanking structural RNA predictions improves the quality of cleaved miRNA predictions and suggest that this strategy can be used to predict other non-coding RNAs undergoing cleavage during maturation.