Pius Brzoska

Bio: Pius Brzoska is an academic researcher from Life Technologies. The author has contributed to research in topics: Population & Genome. The author has an hindex of 13, co-authored 39 publications receiving 5479 citations. Previous affiliations of Pius Brzoska include Thermo Fisher Scientific & Applied Biosystems.

Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis

Abstract: Large intervening non-coding RNAs (lincRNAs) are pervasively transcribed in the genome yet their potential involvement in human disease is not well understood. Recent studies of dosage compensation, imprinting, and homeotic gene expression suggest that individual lincRNAs can function as the interface between DNA and specific chromatin remodelling activities. Here we show that lincRNAs in the HOX loci become systematically dysregulated during breast cancer progression. The lincRNA termed HOTAIR is increased in expression in primary breast tumours and metastases, and HOTAIR expression level in primary tumours is a powerful predictor of eventual metastasis and death. Enforced expression of HOTAIR in epithelial cancer cells induced genome-wide re-targeting of Polycomb repressive complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings indicate that lincRNAs have active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy.

Prospective Genomic Characterization of the German Enterohemorrhagic Escherichia coli O104:H4 Outbreak by Rapid Next Generation Sequencing Technology

Abstract: An ongoing outbreak of exceptionally virulent Shiga toxin (Stx)-producing Escherichia coli O104:H4 centered in Germany, has caused over 830 cases of hemolytic uremic syndrome (HUS) and 46 deaths since May 2011. Serotype O104:H4, which has not been detected in animals, has rarely been associated with HUS in the past. To prospectively elucidate the unique characteristics of this strain in the early stages of this outbreak, we applied whole genome sequencing on the Life Technologies Ion Torrent PGM™ sequencer and Optical Mapping to characterize one outbreak isolate (LB226692) and a historic O104:H4 HUS isolate from 2001 (01-09591). Reference guided draft assemblies of both strains were completed with the newly introduced PGM™ within 62 hours. The HUS-associated strains both carried genes typically found in two types of pathogenic E. coli, enteroaggregative E. coli (EAEC) and enterohemorrhagic E. coli (EHEC). Phylogenetic analyses of 1,144 core E. coli genes indicate that the HUS-causing O104:H4 strains and the previously published sequence of the EAEC strain 55989 show a close relationship but are only distantly related to common EHEC serotypes. Though closely related, the outbreak strain differs from the 2001 strain in plasmid content and fimbrial genes. We propose a model in which EAEC 55989 and EHEC O104:H4 strains evolved from a common EHEC O104:H4 progenitor, and suggest that by stepwise gain and loss of chromosomal and plasmid-encoded virulence factors, a highly pathogenic hybrid of EAEC and EHEC emerged as the current outbreak clone. In conclusion, rapid next-generation technologies facilitated prospective whole genome characterization in the early stages of an outbreak.

Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations.

Abstract: Due to growing throughput and shrinking cost, massively parallel sequencing is rapidly becoming an attractive alternative to microarrays for the genome-wide study of gene expression and copy number alterations in primary tumors. The sequencing of transcripts (RNA-Seq) should offer several advantages over microarray-based methods, including the ability to detect somatic mutations and accurately measure allele-specific expression. To investigate these advantages we have applied a novel, strand-specific RNA-Seq method to tumors and matched normal tissue from three patients with oral squamous cell carcinomas. Additionally, to better understand the genomic determinants of the gene expression changes observed, we have sequenced the tumor and normal genomes of one of these patients. We demonstrate here that our RNA-Seq method accurately measures allelic imbalance and that measurement on the genome-wide scale yields novel insights into cancer etiology. As expected, the set of genes differentially expressed in the tumors is enriched for cell adhesion and differentiation functions, but, unexpectedly, the set of allelically imbalanced genes is also enriched for these same cancer-related functions. By comparing the transcriptomic perturbations observed in one patient to his underlying normal and tumor genomes, we find that allelic imbalance in the tumor is associated with copy number mutations and that copy number mutations are, in turn, strongly associated with changes in transcript abundance. These results support a model in which allele-specific deletions and duplications drive allele-specific changes in gene expression in the developing tumor.

Comparative Analysis of Genome Sequences Covering the Seven Cronobacter Species

Abstract: Background Species of Cronobacter are widespread in the environment and are occasional food-borne pathogens associated with serious neonatal diseases, including bacteraemia, meningitis, and necrotising enterocolitis. The genus is composed of seven species: C. sakazakii, C. malonaticus, C. turicensis, C. dublinensis, C. muytjensii, C. universalis, and C. condimenti. Clinical cases are associated with three species, C. malonaticus, C. turicensis and, in particular, with C. sakazakii multilocus sequence type 4. Thus, it is plausible that virulence determinants have evolved in certain lineages.

Developmental validation of the Quantifiler(®) HP and Trio Kits for human DNA quantification in forensic samples.

Abstract: The quantification of human genomic DNA is a necessary first step in the DNA casework sample analysis workflow. DNA quantification determines optimal sample input amounts for subsequent STR (short tandem repeat) genotyping procedures, as well as being a useful screening tool to identify samples most likely to provide probative genotypic evidence. To better mesh with the capabilities of newest-generation STR analysis assays, the Quantifiler(®) HP and Quantifiler(®) Trio DNA Quantification Kits were designed for greater detection sensitivity and more robust performance with samples that contain PCR inhibitors or degraded DNA. The new DNA quantification kits use multiplex TaqMan(®) assay-based fluorescent probe technology to simultaneously quantify up to three human genomic targets, allowing samples to be assessed for total human DNA, male contributor (i.e., Y-chromosome) DNA, as well as a determination of DNA degradation state. The Quantifiler HP and Trio Kits use multiple-copy loci to allow for significantly improved sensitivity compared to earlier-generation kits that employ single-copy target loci. The kits' improved performance provides better predictive ability for results with downstream, newest-generation STR assays, and their shortened time-to-result allows more efficient integration into the forensic casework analysis workflow.

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

Abstract: A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

Non-coding RNAs in human disease

Abstract: The role of non-coding RNAs (ncRNAs) in disease is best understood for microRNAs in cancer. However, there is increasing interest in the disease-related roles of other ncRNAs — including piRNAs, snoRNAs, T-UCRs and lncRNAs — and in using this knowledge for therapy.

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Abstract: Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.