Comparison of Insertional RNA Editing in Myxomycetes

RNA editing describes the process in which individual or short stretches of nucleotides in a messenger or structural RNA are inserted, deleted, or substituted. A high level of RNA editing has been observed in the mitochondrial genome of Physarum polycephalum. The most frequent editing type in Physarum is the insertion of individual Cs. RNA editing is extremely accurate in Physarum; however, little is known about its mechanism. Here, we demonstrate how analyzing two organisms from the Myxomycetes, namely Physarum polycephalum and Didymium iridis, allows us to test hypotheses about the editing mechanism that can not be tested from a single organism alone. First, we show that using the recently determined full transcriptome information of Physarum dramatically improves the accuracy of computational editing site prediction in Didymium. We use this approach to predict genes in the mitochondrial genome of Didymium and identify six new edited genes as well as one new gene that appears unedited. Next we investigate sequence conservation in the vicinity of editing sites between the two organisms in order to identify sites that harbor the information for the location of editing sites based on increased conservation. Our results imply that the information contained within only nine or ten nucleotides on either side of the editing site (a distance previously suggested through experiments) is not enough to locate the editing sites. Finally, we show that the codon position bias in C insertional RNA editing of these two organisms is correlated with the selection pressure on the respective genes thereby directly testing an evolutionary theory on the origin of this codon bias. Beyond revealing interesting properties of insertional RNA editing in Myxomycetes, our work suggests possible approaches to be used when finding sequence motifs for any biological process fails

Transcript profiling of candidate genes in testis of pigs exhibiting large differences in androstenone levels

<p>Abstract</p> <p>Background</p> <p>Boar taint is an unpleasant odor and flavor of the meat and occurs in a high proportion of uncastrated male pigs. Androstenone, a steroid produced in testis and acting as a sex pheromone regulating reproductive function in female pigs, is one of the main compounds responsible for boar taint. The primary goal of the present investigation was to determine the differential gene expression of selected candidate genes related to levels of androstenone in pigs.</p> <p>Results</p> <p>Altogether 2560 boars from the Norwegian Landrace and Duroc populations were included in this study. Testicle samples from the 192 boars with most extreme high or low levels of androstenone in fat were used for RNA extraction, and 15 candidate genes were selected and analyzed by real-competitive PCR analysis. The genes Cytochrome P450 c17 (<it>CYP17A1</it>), Steroidogenic acute regulatory protein (<it>STAR</it>), Aldo-keto reductase family 1 member C4 (<it>AKR1C4</it>), Short-chain dehydrogenase/reductase family member 4 (<it>DHRS4</it>), Ferritin light polypeptide (<it>FTL</it>), Sulfotransferase family 2A, dehydroepiandrosterone-preferring member 1 (<it>SULT2A1</it>), Cytochrome P450 subfamily XIA polypeptide 1 (<it>CYP11A1</it>), Cytochrome b5 (<it>CYB5A</it>), and 17-beta-Hydroxysteroid dehydrogenase IV (<it>HSD17B4</it>) were all found to be significantly (P < 0.05) up-regulated in high androstenone boars in both Duroc and Landrace. Furthermore, Cytochrome P450 c19A2 (<it>CYP19A2</it>) was down-regulated and progesterone receptor membrane component 1 (<it>PGRMC1</it>) was up-regulated in high-androstenone Duroc boars only, while <it>CYP21 </it>was significantly down-regulated (2.5) in high-androstenone Landrace only. The genes Nuclear Receptor co-activator 4 (<it>NCOA4</it>), Sphingomyrlin phosphodiesterase 1 (<it>SMPD1</it>) and 3β-hydroxysteroid dehydrogenase (<it>HSD3B</it>) were not significantly differentially expressed in any breeds. Additionally, association studies were performed for the genes with one or more detected SNPs. Association between SNP and androstenone level was observed in <it>CYB5A </it>only, suggesting cis-regulation of the differential transcription in this gene.</p> <p>Conclusion</p> <p>A large pig material of highly extreme androstenone levels is investigated. The current study contributes to the knowledge about which genes that is differentially expressed regard to the levels of androstenone in pigs. Results in this paper suggest that several genes are important in the regulation of androstenone level in boars and warrant further evaluation of the above mentioned candidate genes, including analyses in different breeds, identification of causal mutations and possible gene interactions.</p

Carbon Dioxide in Taal Volcanic Lake: A Simple Gasometer for Volcano Monitoring

We report here an increase in the amount of carbon dioxide in Taal lake during the year preceding the January 2020 eruption. Starting in February 2019, the CO2 emissions from the lake increased from background value (700 t day−1) to a flux close to 2,400 t day−1 at the time of the eruption. We show that the lake acts as a highly sensitive gasometer where CO2 (aq) reflects the balance between CO2 supplied to the lake (by hot springs) and CO2 lost by diffusion at air-water interface. The lake waters are extremely enriched in dissolved carbon dioxide with pCO2 values as high as 0.35 atm (350,000 ppmV) equivalent to a CO2(aq) of 9.32 mmol l−1. The residence time of CO2 in the lake is around 1 week which allows for fast detection of change in magma degassing and makes carbon dioxide a very promising tool for volcano monitoring.SCOPUS: le.jinfo:eu-repo/semantics/publishe

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

Background and objectivesNon-invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near-infrared laser light to enable real-time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although&nbsp;RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time-consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging.Study design/materials and methodsA systematic PubMed search was conducted with additional relevant literature obtained from reference lists.ResultsAlgorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers.ConclusionsAI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC