Relaxin gene family in teleosts: phylogeny, syntenic mapping, selective constraint, and expression analysis

<p>Abstract</p> <p>Background</p> <p>In recent years, the relaxin family of signaling molecules has been shown to play diverse roles in mammalian physiology, but little is known about its diversity or physiology in teleosts, an infraclass of the bony fishes comprising ~ 50% of all extant vertebrates. In this paper, 32 relaxin family sequences were obtained by searching genomic and cDNA databases from eight teleost species; phylogenetic, molecular evolutionary, and syntenic data analyses were conducted to understand the relationship and differential patterns of evolution of relaxin family genes in teleosts compared with mammals. Additionally, real-time quantitative PCR was used to confirm and assess the tissues of expression of five relaxin family genes in <it>Danio rerio </it>and <it>in situ </it>hybridization used to assess the site-specific expression of the insulin 3-like gene in <it>D. rerio </it>testis.</p> <p>Results</p> <p>Up to six relaxin family genes were identified in each teleost species. Comparative syntenic mapping revealed that fish possess two paralogous copies of human <it>RLN3</it>, which we call <it>rln3a </it>and <it>rln3b</it>, an orthologue of human <it>RLN2</it>, <it>rln</it>, two paralogous copies of human <it>INSL5</it>, <it>insl5a and insl5b</it>, and an orthologue of human <it>INSL3</it>, <it>insl3</it>. Molecular evolutionary analyses indicated that: <it>rln3a, rln3b </it>and <it>rln </it>are under strong evolutionary constraint, that <it>insl3 </it>has been subject to moderate rates of sequence evolution with two amino acids in <it>insl3/INSL3 </it>showing evidence of positively selection, and that <it>insl5b </it>exhibits a higher rate of sequence evolution than its paralogue <it>insl5a </it>suggesting that it may have been neo-functionalized after the teleost whole genome duplication. Quantitative PCR analyses in <it>D. rerio </it>indicated that <it>rln3a </it>and r<it>ln3b </it>are expressed in brain, <it>insl3 </it>is highly expressed in gonads, and that there was low expression of both <it>insl5 </it>genes in adult zebrafish. Finally, <it>in situ </it>hybridization of <it>insl3 </it>in <it>D. rerio </it>testes showed highly specific hybridization to interstitial Leydig cells.</p> <p>Conclusions</p> <p>Contrary to previous studies, we find convincing evidence that teleosts contain orthologues of four relaxin family peptides. Overall our analyses suggest that in teleosts: 1) <it>rln3 </it>exhibits a similar evolution and expression pattern to mammalian <it>RLN3</it>, 2) <it>insl3 </it>has been subject to positive selection like its mammalian counterpart and shows similar tissue-specific expression in Leydig cells, 3) <it>insl5 </it>genes are highly represented and have a relatively high rate of sequence evolution in teleost genomes, but they exhibited only low levels of expression in adult zebrafish, 4) <it>rln </it>is evolving under very different selective constraints from mammalian <it>RLN</it>. The results presented here should facilitate the development of hypothesis-driven experimental work on the specific roles of relaxin family genes in teleosts.</p

Identification of new particle formation events with deep learning

New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles. Occurrence of NPF events is typically analyzed by researchers manually from particle size distribution data day by day, which is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time that a deep learning method, i.e., transfer learning of a convolutional neural network (CNN), has successfully been used to automatically classify NPF events into different classes directly from particle size distribution images, similarly to how the researchers carry out the manual classification. The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images was used in the training stage of the CNN and the rest of the images for testing the success of the training. The method was utilized for a 15-year-long dataset measured at San Pietro Capofiume (SPC) in Italy. We studied the performance of the training with different training and testing of image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., classes 1 and 2) and nonevent days can be identified with an accuracy of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, and thus overlapping or confusion between different classes occurs. Hence, we cross-validated the learning results of CNN with the expert-made classification. The results show that the overlapping occurs, typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into classes 1 and 2 by CNN, indicating that the manual and CNN classifications are very consistent for most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of quantifiable (i.e., clear events, classes 1 and 2) and nonquantifiable (i.e., weak events, Class  3). This would better describe the difference of those classes: both formation and growth rates can be determined for quantifiable days but not both for nonquantifiable days. Furthermore, we investigated more deeply the days that are classified as clear events by experts and recognized as nonevents by the CNN and vice versa. Clear misclassifications seem to occur more commonly in manual analysis than in the CNN categorization, which is mostly due to the inconsistency in the human-made classification or errors in the booking of the event class. In general, the automatic CNN classifier has a better reliability and repeatability in NPF event classification than human-made classification and, thus, the transfer-learned pretrained CNNs are powerful tools to analyze long-term datasets. The developed NPF event classifier can be easily utilized to analyze any long-term datasets more accurately and consistently, which helps us to understand in detail aerosol–climate interactions and the long-term effects of climate change on NPF in the atmosphere. We encourage researchers to use the model in other sites. However, we suggest that the CNN should be transfer learned again for new site data with a minimum of ca. 150 figures per class to obtain good enough classification results, especially if the size distribution evolution differs from training data. In the future, we will utilize the method for data from other sites, develop it to analyze more parameters and evaluate how successfully CNN could be trained with synthetic NPF event data.</p

Role of T198 Modification in the Regulation of p27Kip1 Protein Stability and Function

The tumor suppressor gene p27Kip1 plays a fundamental role in human cancer progression. Its expression and/or functions are altered in almost all the different tumor histotype analyzed so far. Recently, it has been demonstrated that the tumor suppression function of p27 resides not only in the ability to inhibit Cyclins/CDKs complexes through its N-terminal domain but also in the capacity to modulate cell motility through its C-terminal portion. Particular interest has been raised by the last amino-acid, (Threonine 198) in the regulation of both protein stability and cell motility

LeishVet update and recommendations on feline leishmaniosis

Limited data is available on feline leishmaniosis (FeL) caused by Leishmania infantum worldwide. The LeishVet group presents in this report a review of the current knowledge on FeL, the epidemiological role of the cat in L. infantum infection, clinical manifestations, and recommendations on diagnosis, treatment and monitoring, prognosis and prevention of infection, in order to standardize the management of this disease in cats. The consensus of opinions and recommendations was formulated by combining a comprehensive review of evidence-based studies and case reports, clinical experience and critical consensus discussions. While subclinical feline infections are common in areas endemic for canine leishmaniosis, clinical illness due to L. infantum in cats is rare. The prevalence rates of feline infection with L. infantum in serological or molecular-based surveys range from 0 % to more than 60 %. Cats are able to infect sand flies and, therefore, they may act as a secondary reservoir, with dogs being the primary natural reservoir. The most common clinical signs and clinicopathological abnormalities compatible with FeL include lymph node enlargement and skin lesions such as ulcerative, exfoliative, crusting or nodular dermatitis (mainly on the head or distal limbs), ocular lesions (mainly uveitis), feline chronic gingivostomatitis syndrome, mucocutaneous ulcerative or nodular lesions, hypergammaglobulinaemia and mild normocytic normochromic anaemia. Clinical illness is frequently associated with impaired immunocompetence, as in case of retroviral coinfections or immunosuppressive therapy. Diagnosis is based on serology, polymerase chain reaction (PCR), cytology, histology, immunohistochemistry (IHC) or culture. If serological testing is negative or low positive in a cat with clinical signs compatible with FeL, the diagnosis of leishmaniosis should not be excluded and additional diagnostic methods (cytology, histology with IHC, PCR, culture) should be employed. The most common treatment used is allopurinol. Meglumine antimoniate has been administered in very few reported cases. Both drugs are administered alone and most cats recover clinically after therapy. Follow-up of treated cats with routine laboratory tests, serology and PCR is essential for prevention of clinical relapses. Specific preventative measures for this infection in cats are currently not available

MISE EN EVIDENCE D'UNE ACTIVITE DE TYPE CASEINE KINASE-II DANS LE NOYAU DES OVOCYTES DE XENOPE, PHOSPHORYLANT IN VITRO UNE ISOFORME BASIQUE DE L'α-TUBULINE

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