Characterization of the diversity of type IV secretion system-encoding plasmids in Acinetobacter.

The multi-drug resistant pathogen Acinetobacter baumannii has gained global attention as an important clinical challenge. Owing to its ability to survive on surfaces, its capacity for horizontal gene transfer, and its resistance to front-line antibiotics, A. baumannii has established itself as a successful pathogen. Bacterial conjugation is a central mechanism for pathogen evolution. The epidemic multidrug-resistant A. baumannii ACICU harbours a plasmid encoding a Type IV Secretion System (T4SS) with homology to the E. coli F-plasmid, and plasmids with homologous gene clusters have been identified in several A. baumannii sequence types. However the genetic and host strain diversity, global distribution, and functional ability of this group of plasmids is not fully understood. Using systematic analysis, we show that pACICU2 belongs to a group of almost 120 T4SS-encoding plasmids within four different species of Acinetobacter and one strain of Klebsiella pneumoniae from human and environmental origin, and globally distributed across 20 countries spanning 4 continents. Genetic diversity was observed both outside and within the T4SS-encoding cluster, and 47% of plasmids harboured resistance determinants, with two plasmids harbouring eleven. Conjugation studies with an extensively drug-resistant (XDR) strain showed that the XDR plasmid could be successfully transferred to a more divergent A. baumanii, and transconjugants exhibited the resistance phenotype of the plasmid. Collectively, this demonstrates that these T4SS-encoding plasmids are globally distributed and more widespread among Acinetobacter than previously thought, and that they represent an important potential reservoir for future clinical concern.

Differential DNA damage repair and PARP inhibitor vulnerability of the mammary epithelial lineages.

It is widely assumed that all normal somatic cells can equally perform homologous recombination (HR) and non-homologous end joining in the DNA damage response (DDR). Here, we show that the DDR in normal mammary gland inherently depends on the epithelial cell lineage identity. Bioinformatics, post-irradiation DNA damage repair kinetics, and clonogenic assays demonstrated luminal lineage exhibiting a more pronounced DDR and HR repair compared to the basal lineage. Consequently, basal progenitors were far more sensitive to poly(ADP-ribose) polymerase inhibitors (PARPis) in both mouse and human mammary epithelium. Furthermore, PARPi sensitivity of murine and human breast cancer cell lines as well as patient-derived xenografts correlated with their molecular resemblance to the mammary progenitor lineages. Thus, mammary epithelial cells are intrinsically divergent in their DNA damage repair capacity and PARPi vulnerability, potentially influencing the clinical utility of this targeted therapy.

IPO11 regulates the nuclear import of BZW1/2 and is necessary for AML cells and stem cells.

AML cells are arranged in a hierarchy with stem/progenitor cells giving rise to more differentiated bulk cells. Despite the importance of stem/progenitors in the pathogenesis of AML, the determinants of the AML stem/progenitor state are not fully understood. Through a comparison of genes that are significant for growth and viability of AML cells by way of a CRISPR screen, with genes that are differentially expressed in leukemia stem cells (LSC), we identified importin 11 (IPO11) as a novel target in AML. Importin 11 (IPO11) is a member of the importin ß family of proteins that mediate transport of proteins across the nuclear membrane. In AML, knockdown of IPO11 decreased growth, reduced engraftment potential of LSC, and induced differentiation. Mechanistically, we identified the transcription factors BZW1 and BZW2 as novel cargo of IPO11. We further show that BZW1/2 mediate a transcriptional signature that promotes stemness and survival of LSC. Thus, we demonstrate for the first time how specific cytoplasmic-nuclear regulation supports stem-like transcriptional signature in relapsed AML.

Orchestrating and sharing large multimodal data for transparent and reproducible research.

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts.

Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. SIGNIFICANCE: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX.See related commentary by Meehan, p. 4324.

Software for the Integration of Multiomics Experiments in Bioconductor.

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.