Highly conserved molecular pathways, including Wnt signaling, promote functional recovery from spinal cord injury in lampreys

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 742, doi:10.1038/s41598-017-18757-1.In mammals, spinal cord injury (SCI) leads to dramatic losses in neurons and synaptic connections, and consequently function. Unlike mammals, lampreys are vertebrates that undergo spontaneous regeneration and achieve functional recovery after SCI. Therefore our goal was to determine the complete transcriptional responses that occur after SCI in lampreys and to identify deeply conserved pathways that promote regeneration. We performed RNA-Seq on lamprey spinal cord and brain throughout the course of functional recovery. We describe complex transcriptional responses in the injured spinal cord, and somewhat surprisingly, also in the brain. Transcriptional responses to SCI in lampreys included transcription factor networks that promote peripheral nerve regeneration in mammals such as Atf3 and Jun. Furthermore, a number of highly conserved axon guidance, extracellular matrix, and proliferation genes were also differentially expressed after SCI in lampreys. Strikingly, ~3% of differentially expressed transcripts belonged to the Wnt pathways. These included members of the Wnt and Frizzled gene families, and genes involved in downstream signaling. Pharmacological inhibition of Wnt signaling inhibited functional recovery, confirming a critical role for this pathway. These data indicate that molecular signals present in mammals are also involved in regeneration in lampreys, supporting translational relevance of the model.We gratefully acknowledge support from the National Institutes of Health (R03NS078519 to OB; R01GM104123 to JJS; R01NS078165 to JRM), The Feinstein Institute for Medical Research and The Marine Biological Laboratory, including the Charles Evans Foundation Research Award, the Albert and Ellen Grass Foundation Faculty Research Award, and The Eugene and Millicent Bell Fellowship Fund in Tissue Engineering

System matrix analysis for computed tomography imaging

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data.This work has been supported by Universitat Politecnica de Valencia and partially funded by ANITRAN PROMETEOII/2014/008 of the Generalitat Valenciana of Spain.Flores, LA.; Vicent Vidal; Verdú Martín, GJ. (2015). System matrix analysis for computed tomography imaging. PLoS ONE. 10(11):1-12. https://doi.org/10.1371/journal.pone.0143202S1121011Herman, G. T. (2009). Fundamentals of Computerized Tomography. Advances in Pattern Recognition. doi:10.1007/978-1-84628-723-7Wang, G., Yu, H., & De Man, B. (2008). An outlook on x-ray CT research and development. Medical Physics, 35(3), 1051-1064. doi:10.1118/1.2836950Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289-1306. doi:10.1109/tit.2006.871582Candès, E. J., Romberg, J. K., & Tao, T. (2006). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207-1223. doi:10.1002/cpa.20124Yu, H., & Wang, G. (2010). A soft-threshold filtering approach for reconstruction from a limited number of projections. Physics in Medicine and Biology, 55(13), 3905-3916. doi:10.1088/0031-9155/55/13/022Andersen, A. H., & Kak, A. C. (1984). Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm. Ultrasonic Imaging, 6(1), 81-94. doi:10.1177/016173468400600107Flores, L. A., Vidal, V., Mayo, P., Rodenas, F., & Verdú, G. (2013). CT Image Reconstruction Based on GPUs. Procedia Computer Science, 18, 1412-1420. doi:10.1016/j.procs.2013.05.308Flores, L., Vidal, V., & Verdú, G. (2015). Iterative Reconstruction from Few-view Projections. Procedia Computer Science, 51, 703-712. doi:10.1016/j.procs.2015.05.188Cibeles Mora Mora, T. Métodos de reconstrucción volumétrica algebraica de imágenes tomográficas. 2008. Tesis PhD. Spain.Siddon, R. L. (1985). Fast calculation of the exact radiological path for a three-dimensional CT array. Medical Physics, 12(2), 252-255. doi:10.1118/1.595715Joseph, P. M. (1982). An Improved Algorithm for Reprojecting Rays through Pixel Images. IEEE Transactions on Medical Imaging, 1(3), 192-196. doi:10.1109/tmi.1982.4307572Paige, C. C., & Saunders, M. A. (1982). LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software, 8(1), 43-71. doi:10.1145/355984.355989Higham, N. J. (1993). The Accuracy of Floating Point Summation. SIAM Journal on Scientific Computing, 14(4), 783-799. doi:10.1137/091405

Estrogen receptor transcription and transactivation: Estrogen receptor alpha and estrogen receptor beta - regulation by selective estrogen receptor modulators and importance in breast cancer

Estrogens display intriguing tissue-selective action that is of great biomedical importance in the development of optimal therapeutics for the prevention and treatment of breast cancer, for menopausal hormone replacement, and for fertility regulation. Certain compounds that act through the estrogen receptor (ER), now referred to as selective estrogen receptor modulators (SERMs), can demonstrate remarkable differences in activity in the various estrogen target tissues, functioning as agonists in some tissues but as antagonists in others. Recent advances elucidating the tripartite nature of the biochemical and molecular actions of estrogens provide a good basis for understanding these tissue-selective actions. As discussed in this thematic review, the development of optimal SERMs should now be viewed in the context of two estrogen receptor subtypes, ERα and ERβ, that have differing affinities and responsiveness to various SERMs, and differing tissue distribution and effectiveness at various gene regulatory sites. Cellular, biochemical, and structural approaches have also shown that the nature of the ligand affects the conformation assumed by the ER-ligand complex, thereby regulating its state of phosphorylation and the recruitment of different coregulator proteins. Growth factors and protein kinases that control the phosphorylation state of the complex also regulate the bioactivity of the ER. These interactions and changes determine the magnitude of the transcriptional response and the potency of different SERMs. As these critical components are becoming increasingly well defined, they provide a sound basis for the development of novel SERMs with optimal profiles of tissue selectivity as medical therapeutic agents

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Background Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P Conclusion Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.Peer reviewe

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article