A Cre-conditional MYCN-driven neuroblastoma mouse model as an improved tool for preclinical studies

Neuroblastoma, a childhood cancer that originates from neural crest-derived cells, is the most common deadly solid tumor of infancy. Amplification of the MYCN oncogene, which occurs in approximately 20-25% of human neuroblastomas, is the most prominent genetic marker of high-stage disease. The availability of valid preclinical in vivo models is a prerequisite to develop novel targeted therapies. We here report on the generation of transgenic mice with Cre-conditional induction of MYCN in dopamine β-hydroxylase-expressing cells, termed LSL-MYCN;Dbh-iCre. These mice develop neuroblastic tumors with an incidence of >75%, regardless of strain background. Molecular profiling of tumors revealed upregulation of the MYCN-dependent miR-17-92 cluster as well as expression of neuroblastoma marker genes, including tyrosine hydroxylase and the neural cell adhesion molecule 1. Gene set enrichment analyses demonstrated significant correlation with MYC-associated expression patterns. Array comparative genome hybridization showed that chromosomal aberrations in LSL-MYCN;Dbh-iCre tumors were syntenic to those observed in human neuroblastomas. Treatment of a cell line established from a tumor derived from a LSL-MYCN;Dbh-iCre mouse with JQ1 or MLN8237 reduced cell viability and demonstrated oncogene addiction to MYCN. Here we report establishment of the first Cre-conditional human MYCN-driven mouse model for neuroblastoma that closely recapitulates the human disease with respect to tumor localization, histology, marker expression and genomic make up. This mouse model is a valuable tool for further functional studies and to assess the effect of targeted therapies

A Rho Scaffold Integrates the Secretory System with Feedback Mechanisms in Regulation of Auxin Distribution

In plants, auxin distribution and tissue patterning are coordinated via a feedback loop involving the auxin-regulated cell polarity factor ICR1 and the secretory machinery

Diagnostic accuracy of a clinical diagnosis of idiopathic pulmonary fibrosis: An international case-cohort study

We conducted an international study of idiopathic pulmonary fibrosis (IPF) diagnosis among a large group of physicians and compared their diagnostic performance to a panel of IPF experts. A total of 1141 respiratory physicians and 34 IPF experts participated. Participants evaluated 60 cases of interstitial lung disease (ILD) without interdisciplinary consultation. Diagnostic agreement was measured using the weighted kappa coefficient (\u3baw). Prognostic discrimination between IPF and other ILDs was used to validate diagnostic accuracy for first-choice diagnoses of IPF and were compared using the Cindex. A total of 404 physicians completed the study. Agreement for IPF diagnosis was higher among expert physicians (\u3baw=0.65, IQR 0.53-0.72, p20 years of experience (C-index=0.72, IQR 0.0-0.73, p=0.229) and non-university hospital physicians with more than 20 years of experience, attending weekly MDT meetings (C-index=0.72, IQR 0.70-0.72, p=0.052), did not differ significantly (p=0.229 and p=0.052 respectively) from the expert panel (C-index=0.74 IQR 0.72-0.75). Experienced respiratory physicians at university-based institutions diagnose IPF with similar prognostic accuracy to IPF experts. Regular MDT meeting attendance improves the prognostic accuracy of experienced non-university practitioners to levels achieved by IPF experts

Non-irradiation-derived reactive oxygen species (ROS) and cancer: therapeutic implications

Owing to their chemical reactivity, radicals have cytocidal properties. Destruction of cells by irradiation-induced radical formation is one of the most frequent interventions in cancer therapy. An alternative to irradiation-induced radical formation is in principle drug-induced formation of radicals, and the formation of toxic metabolites by enzyme catalysed reactions. Although these developments are currently still in their infancy, they nevertheless deserve consideration. There are now numerous examples known of conventional anti-cancer drugs that may at least in part exert cytotoxicity by induction of radical formation. Some drugs, such as arsenic trioxide and 2-methoxy-estradiol, were shown to induce programmed cell death due to radical formation. Enzyme-catalysed radical formation has the advantage that cytotoxic products are produced continuously over an extended period of time in the vicinity of tumour cells. Up to now the enzymatic formation of toxic metabolites has nearly exclusively been investigated using bovine serum amine oxidase (BSAO), and spermine as substrate. The metabolites of this reaction, hydrogen peroxide and aldehydes are cytotoxic. The combination of BSAO and spermine is not only able to prevent tumour cell growth, but prevents also tumour growth, particularly well if the enzyme has been conjugated with a biocompatible gel. Since the tumour cells release substrates of BSAO, the administration of spermine is not required. Combination with cytotoxic drugs, and elevation of temperature improves the cytocidal effect of spermine metabolites. The fact that multidrug resistant cells are more sensitive to spermine metabolites than their wild type counterparts makes this new approach especially attractive, since the development of multidrug resistance is one of the major problems of conventional cancer therapy

Southern African Large Telescope Spectroscopy of BL Lacs for the CTA project

In the last two decades, very-high-energy gamma-ray astronomy has reached maturity: over 200 sources have been detected, both Galactic and extragalactic, by ground-based experiments. At present, Active Galactic Nuclei (AGN) make up about 40% of the more than 200 sources detected at very high energies with ground-based telescopes, the majority of which are blazars, i.e. their jets are closely aligned with the line of sight to Earth and three quarters of which are classified as high-frequency peaked BL Lac objects. One challenge to studies of the cosmological evolution of BL Lacs is the difficulty of obtaining redshifts from their nearly featureless, continuum-dominated spectra. It is expected that a significant fraction of the AGN to be detected with the future Cherenkov Telescope Array (CTA) observatory will have no spectroscopic redshifts, compromising the reliability of BL Lac population studies, particularly of their cosmic evolution. We started an effort in 2019 to measure the redshifts of a large fraction of the AGN that are likely to be detected with CTA, using the Southern African Large Telescope (SALT). In this contribution, we present two results from an on-going SALT program focused on the determination of BL Lac object redshifts that will be relevant for the CTA observatory

Identification of a Novel Class of Farnesylation Targets by Structure-Based Modeling of Binding Specificity

Farnesylation is an important post-translational modification catalyzed by farnesyltransferase (FTase). Until recently it was believed that a C-terminal CaaX motif is required for farnesylation, but recent experiments have revealed larger substrate diversity. In this study, we propose a general structural modeling scheme to account for peptide binding specificity and recapitulate the experimentally derived selectivity profile of FTase in vitro. In addition to highly accurate recovery of known FTase targets, we also identify a range of novel potential targets in the human genome, including a new substrate class with an acidic C-terminal residue (CxxD/E). In vitro experiments verified farnesylation of 26/29 tested peptides, including both novel human targets, as well as peptides predicted to tightly bind FTase. This study extends the putative range of biological farnesylation substrates. Moreover, it suggests that the ability of a peptide to bind FTase is a main determinant for the farnesylation reaction. Finally, simple adaptation of our approach can contribute to more accurate and complete elucidation of peptide-mediated interactions and modifications in the cell

Rapid Sampling of Molecular Motions with Prior Information Constraints

Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion

Phylogenetic and functional marker genes to study ammonia-oxidizing microorganisms (AOM) in the environment

The oxidation of ammonia plays a significant role in the transformation of fixed nitrogen in the global nitrogen cycle. Autotrophic ammonia oxidation is known in three groups of microorganisms. Aerobic ammonia-oxidizing bacteria and archaea convert ammonia into nitrite during nitrification. Anaerobic ammonia-oxidizing bacteria (anammox) oxidize ammonia using nitrite as electron acceptor and producing atmospheric dinitrogen. The isolation and cultivation of all three groups in the laboratory are quite problematic due to their slow growth rates, poor growth yields, unpredictable lag phases, and sensitivity to certain organic compounds. Culture-independent approaches have contributed importantly to our understanding of the diversity and distribution of these microorganisms in the environment. In this review, we present an overview of approaches that have been used for the molecular study of ammonia oxidizers and discuss their application in different environments