The Goddard and Saturn Genes Are Essential for Drosophila Male Fertility and May Have Arisen de Novo

© 2017 The Author. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. New genes arise through a variety of mechanisms, including the duplication of existing genes and the de novo birth of genes from noncoding DNA sequences. While there are numerous examples of duplicated genes with important functional roles, the functions of de novo genes remain largely unexplored. Many newly evolved genes are expressed in the male reproductive tract, suggesting that these evolutionary innovations may provide advantages to males experiencing sexual selection. Using testis-specific RNA interference, we screened 11 putative de novo genes in Drosophila melanogaster for effects on male fertility and identified two, goddard and saturn, that are essential for spermatogenesis and sperm function. Goddard knockdown (KD) males fail to produce mature sperm, while saturn KD males produce few sperm, and these function inefficiently once transferred to females. Consistent with a de novo origin, both genes are identifiable only in Drosophila and are predicted to encode proteins with no sequence similarity to any annotated protein. However, since high levels of divergence prevented the unambiguous identification of the noncoding sequences fromwhich each gene arose, we consider goddard and saturn to be putative de novo genes. Within Drosophila, both genes have been lost in certain lineages, but show conserved, male-specific patterns of expression in the species in which they are found. Goddard is consistently found in single-copy and evolves under purifying selection. In contrast, saturn has diversified through gene duplication and positive selection. These data suggest that de novo genes can acquire essential roles in male reproduction

A distributed multiscale computation of a tightly coupled model using the Multiscale Modeling Language

AbstractNature is observed at all scales; with multiscale modeling, scientists bring together several scales for a holistic analysis of a phenomenon. The models on these different scales may require significant but also heterogeneous computational resources, creating the need for distributed multiscale computing. A particularly demanding type of multiscale models, tightly coupled, brings with it a number of theoretical and practical issues. In this contribution, a tightly coupled model of in-stent restenosis is first theoretically examined for its multiscale merits using the Multiscale Modeling Language (MML); this is aided by a toolchain consisting of MAPPER Memory (MaMe), the Multiscale Application Designer (MAD), and Gridspace Experiment Workbench. It is implemented and executed with the general Multiscale Coupling Library and Environment (MUSCLE). Finally, it is scheduled amongst heterogeneous infrastructures using the QCG-Broker. This marks the first occasion that a tightly coupled application uses distributed multiscale computing in such a general way

Simultaneous Measurement of the Dissolution Kinetics of Responsive DNA Hydrogels at Multiple Length Scales

Recent years have seen increasing study of stimulus-responsive hydrogels constructed from aptamer-connected DNA building blocks. Presumably due to a lack of simple, quantitative tools with which to measure gel responsiveness, however, the literature describing these materials is largely qualitative. In response we demonstrate here simple, time-resolved, multiscale methods for measuring the response kinetics of these materials. Specifically, by employing trace amounts of fluorophore-quencher labeled crosslinkers and the rheology of entrapped fluorescent particles we simultaneously measure dissolution at molecular, hundred-nanometer, and hundred-micron length-scales. For our test-bed system, an adenine-responsive hydrogel, we find biphasic response kinetics dependent on both effector concentration and depth within the gel and a dissolution pattern uniform at scales longer than a few times the monomer-monomer distance. Likewise, we find that, in agreement with theoretical predictions, dissolution kinetics over the hundred nanometer length scale exhibit a power-law-like dependence on the fraction of disrupted crosslinks before a distinct crossover from solid-like to liquid-like behavio

Walking a Supramolecular Tightrope: A Self-Assembled Dodecamer from an 8-Aryl-2′-deoxyguanosine Derivative

Guanosine quadruplexes (GQs) have emerged in recent years as key players in the development of promising functional nanostruc-tures.1 GQs are formed by the self-assembly of guanosine subunits into planar tetramers (G-tetrads) that stack on each other, assisted by the complexation of a metal cation such as K+ or Na+. Alternatively, GQs can also form via the folding of G-rich oligonucleotides (e.g., DNA, RNA) leading to monomeric, dimeric, and tetrameric structures via the association of one, two, or four oligonucleotides, respectively.1d,2 In the latter, the number of G-tetrads is primarily controlled by the sequence (intrinsic param-eter) of the oligonucleotide, whereas, in the former, such control can be primarily achieved by adjusting extrinsic parameters (e.g., concentration, temperature, solvent,3 the cation template,4 and/or its counteranion5). Controlling the molecularity via intrinsic parameters (i.e., structural information in the supramolecula

Support for multiscale simulations with molecular dynamics

We present a reusable solution that supports users in combining single-scale models to create a multiscale application. Our approach applies several multiscale programming tools to allow users to compose multiscale applications using a graphical interface, and provides an easy way to execute these multiscale applications on international production infrastructures. Our solution extends the general purpose scripting approach of the GridSpace platform with simple mechanisms for accessing production resources, provided by the Application Hosting Environment (AHE). We apply our support solution to construct and execute a multiscale simulation of clay-polymer nanocomposite materials, and showcase its benefit in reducing the effort required to do a number of time-intensive user tasks. © 2013 The Authors. Published by Elsevier B.V

Is there loss or qualitative changes in the expression of thyroid peroxidase protein in thyroid epithelial cancer?

There is disagreement concerning the expression of thyroid peroxidase (TPO) in thyroid cancer, some studies finding qualitative as well as quantitative differences compared to normal tissue. To investigate TPO protein expression and its antigenic properties, TPO was captured from a solubilizate of thyroid microsomes by a panel of murine anti-TPO monoclonal antibodies and detected with a panel of anti-human TPO IgGκ Fab. TPO protein expression in 30 samples of malignant thyroid tissue was compared with TPO from adjacent normal tissues. Virtual absence of TPO expression was observed in 8 cases. In the remaining 22 malignant thyroid tumours the TPO protein level varied considerably from normal to nearly absent when compared to normal thyroid tissue or tissues from patients with Graves' disease (range less than 0.5 to more than 12.5 μg mg−1 of protein). When expressed TPO displayed similar epitopes, to that of TPO from Graves' disease tissue. The results obtained by the TPO capturing method were confirmed by SDS-PAGE and Western blot analysis with both microsomes and their solubilizates. The present results show that in about two-thirds of differentiated thyroid carcinomas, TPO protein is expressed, albeit to a more variable extent than normal; when present, TPO in malignant tissues is immunologically normal. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours