The PARP inhibitor AZD2461 provides insights into the role of PARP3 inhibition for both synthetic lethality and tolerability with chemotherapy in preclinical models

The PARP inhibitor AZD2461 was developed as a next-generation agent following olaparib, the first PARP inhibitor approved for cancer therapy. In BRCA1-deficient mouse models, olaparib resistance predominantly involves overexpression of P-glycoprotein,so AZD2461 was developed as a poor substrate for drug transporters. Here we demonstrate the efficacy of this compound against olaparib-resistant tumors that overexpress P-glycoprotein. In addition, AZD2461 was better tolerated in combination with chemotherapy than olaparib in mice, which suggests that AZD2461 could have significant advantages over olaparib in the clinic. However, this superior toxicity profile did not extend to rats. Investigations of this difference revealed a differential PARP3 inhibitory activity for each compound and a higher level of PARP3 expression in bone marrow cells from mice as compared with rats and humans. Our findings have implications for the use of mouse models to assess bone marrow toxicity for DNA-damaging agents and inhibitors of the DNA damage response. Finally, structural modeling of the PARP3-active site with different PARP inhibitors also highlights the potential to develop compounds with different PARP family member specificity profiles for optimal antitumor activity and tolerability

Metabolic Profiling of a Mapping Population Exposes New Insights in the Regulation of Seed Metabolism and Seed, Fruit, and Plant Relations

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping

Tuberculosis and HIV Co-Infection

Tuberculosis (TB) and HIV co-infections place an immense burden on health care systems and pose particular diagnostic and therapeutic challenges. Infection with HIV is the most powerful known risk factor predisposing for Mycobacterium tuberculosis infection and progression to active disease, which increases the risk of latent TB reactivation 20-fold. TB is also the most common cause of AIDS-related death. Thus, M. tuberculosis and HIV act in synergy, accelerating the decline of immunological functions and leading to subsequent death if untreated. The mechanisms behind the breakdown of the immune defense of the co-infected individual are not well known. The aim of this review is to highlight immunological events that may accelerate the development of one of the two diseases in the presence of the co-infecting organism. We also review possible animal models for studies of the interaction of the two pathogens, and describe gaps in knowledge and needs for future studies to develop preventive measures against the two diseases

Patient-derived xenograft (PDX) models in basic and translational breast cancer research

Patient-derived xenograft (PDX) models of a growing spectrum of cancers are rapidly supplanting long-established traditional cell lines as preferred models for conducting basic and translational preclinical research. In breast cancer, to complement the now curated collection of approximately 45 long-established human breast cancer cell lines, a newly formed consortium of academic laboratories, currently from Europe, Australia, and North America, herein summarizes data on over 500 stably transplantable PDX models representing all three clinical subtypes of breast cancer (ER+, HER2+, and "Triple-negative" (TNBC)). Many of these models are well-characterized with respect to genomic, transcriptomic, and proteomic features, metastatic behavior, and treatment response to a variety of standard-of-care and experimental therapeutics. These stably transplantable PDX lines are generally available for dissemination to laboratories conducting translational research, and contact information for each collection is provided. This review summarizes current experiences related to PDX generation across participating groups, efforts to develop data standards for annotation and dissemination of patient clinical information that does not compromise patient privacy, efforts to develop complementary data standards for annotation of PDX characteristics and biology, and progress toward "credentialing" of PDX models as surrogates to represent individual patients for use in preclinical and co-clinical translational research. In addition, this review highlights important unresolved questions, as well as current limitations, that have hampered more efficient generation of PDX lines and more rapid adoption of PDX use in translational breast cancer research