Plantar warts on Tollund Man's feet (Denmark, fourth century BC). Limits of retrospective dermatological diagnosis

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Ichthyosis with confetti presenting as collodion baby: a novel mutation in KRT10

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Genomics of sex hormone receptor signaling in hepatic sexual dimorphism

Publication date: Available online 26 May 2017
Source:Molecular and Cellular Endocrinology
Author(s): Daoshan Zheng, Xiao Wang, Per Antonson, Jan-Åke Gustafsson, Zhaoyu Li
The liver plays a crucial role in a variety of physiological processes. Sexual dimorphism is markedly defined in liver disorders, such as fatty liver diseases and liver cancer, but barely addressed in the normal liver. Distinct sex hormone signaling between male and female livers is the major driving factor for hepatic sexual dimorphism. Over 6000 genes are differently expressed between male and female livers in mice. Here we address how sex hormone receptors, estrogen receptor alpha (ERα) and androgen receptor (AR), mediate sexually dimorphic gene expression in mouse livers. We identified 5192 ERα target genes and 4154 AR target genes using ChIP-Seq. Using liver-specific ERα or AR knockout mice, we further identified direct and functional target genes of ERα (123 genes) and AR (151 genes) that contribute to hepatic sexual dimorphism. We also found that the most significant sexually dimorphic gene expression was initiated at birth by comparing hepatic gene expression data from the embryonic stage E10.5 to the postnatal stage P60 during liver development. Overall, our study indicates that sex hormone receptor signaling drives sexual dimorphism of hepatic gene expression throughout liver development.



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A comparison of the sexually dimorphic dexamethasone transcriptome in mouse cerebral cortical and hypothalamic embryonic neural stem cells

Publication date: Available online 26 May 2017
Source:Molecular and Cellular Endocrinology
Author(s): Krystle A. Frahm, Jacob K. Waldman, Soumya Luthra, Anthony C. Rudine, A. Paula Monaghan-Nichols, Uma R. Chandran, Donald B. DeFranco
Fetal exposure to synthetic glucocorticoids reprograms distinct neural circuits in the developing brain, often in a sex-specific manner, via mechanisms that remain poorly understood. To reveal whether such reprogramming is associated with select molecular signatures, we characterized the transcriptome of primary, embryonic mouse cerebral cortical and hypothalamic neural progenitor/stem cells derived from individual male and female embryos exposed to the synthetic glucocorticoid, dexamethasone. Gene expression profiling by RNA-Seq identified differential expression of common and unique genes based upon brain region, sex, and/or dexamethasone exposure. These gene expression datasets provide a unique resource that will inform future studies examining the molecular mechanisms responsible for region- and sex-specific reprogramming of the fetal brain brought about by in utero exposure to excess glucocorticoids.



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Integrated omics approaches to characterize a nuclear receptor corepressor-associated histone deacetylase in mouse skeletal muscle

Publication date: Available online 26 May 2017
Source:Molecular and Cellular Endocrinology
Author(s): Yingyun Gong, Rui Cao, Guolian Ding, Sungguan Hong, Wenjun Zhou, Wenyun Lu, Manashree Damle, Bin Fang, Chuhan C. Wang, Justin Qian, Natasha Lie, Cristina Lanzillotta, Joshua D. Rabinowitz, Zheng Sun
Nuclear receptors regulate gene expression by differential binding to coactivators or corepressors in a ligand-dependent manner, which further recruits a set of epigenome-modifying enzymes that remodel chromatin conformation. Histone acetylation is a major epigenomic change controlled by histone acetyltransferases (HATs) and histone deacetylases (HDACs). HDAC3 is the only HDAC that confers the enzymatic activity to the complexes nucleated by nuclear receptor corepressors NCoR and SMRT. To address the metabolic function of HDAC3, we have deleted it specifically in mouse skeletal muscles. We have performed the following omics profiling in skeletal muscles of these mice: (1) RNA-seq profiling of total RNA; (2) Global nuclear run-on (GRO-seq) analysis of nascent RNAs; (3) Chromatin immuno-precipitation (ChIP-seq) of HDAC3 at both early evening and early morning; (4) proteomics profiling with mass spectrometry; (5) snap-shot metabolomics profiling of water-soluble metabolites at the basal condition; (6) snap-shot metabolomics profiling of lipid species at the basal condition; (7) kinetic fluxomics analysis of glucose utilization using ¹³C6-glucose in vivo during treadmill running exercise. These approaches have provided several novel insights into how nuclear receptors regulate circadian rhythm of skeletal muscle fuel metabolism, which has been published elsewhere. Here we present the original datasets and technical details during the execution, analysis, and interpretation of these omics studies.



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Sexual murder typologies: A systematic review

Publication date: Available online 26 May 2017
Source:Aggression and Violent Behavior
Author(s): Tamsin Higgs, Adam J. Carter, Ruth J. Tully, Kevin D. Browne
Establishing an empirically valid typology of sexual murder is necessary for developing theory, risk assessment, and intervention. Results from all empirical studies conducted between 1970 and 2016, drawing on information pertaining to >700 sexual murderers, were collated to provide a definitive best evidence synthesis elucidating the overall patterns and motives underlying sexual murder. Three subtypes of sexual murder were consistently found. The term sexualized murder is proposed, to refer to those crimes in which killing is functionally related to the sexual element of the offence. Grievance murder is driven by angry schema and an excessively aggressive response style. Finally, rape murder involves only an indirect association between the sexual offence and killing. Factors distinguishing these subtypes are discussed, and attention is directed towards gaps in knowledge, particularly in relation to biopsychosocial and criminal career factors that remain under-researched.



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Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy

Publication date: Available online 26 May 2017
Source:Academic Radiology
Author(s): Gopichandh Danala, Theresa Thai, Camille C. Gunderson, Katherine M. Moxley, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
Rationale and ObjectivesThe study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients.Materials and MethodsA dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared.ResultsThe highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively.ConclusionsThis study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.



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