The bornavirus-derived human protein EBLN1 promotes efficient cell cycle transit, microtubule organisation and genome stability.

It was recently discovered that vertebrate genomes contain multiple endogenised nucleotide sequences derived from the non-retroviral RNA bornavirus. Strikingly, some of these elements have been evolutionary maintained as open reading frames in host genomes for over 40 million years, suggesting that some endogenised bornavirus-derived elements (EBL) might encode functional proteins. EBLN1 is one such element established through endogenisation of the bornavirus N gene (BDV N). Here, we functionally characterise human EBLN1 as a novel regulator of genome stability. Cells depleted of human EBLN1 accumulate DNA damage both under non-stressed conditions and following exogenously induced DNA damage. EBLN1-depleted cells also exhibit cell cycle abnormalities and defects in microtubule organisation as well as premature centrosome splitting, which we attribute in part, to improper localisation of the nuclear envelope protein TPR. Our data therefore reveal that human EBLN1 possesses important cellular functions within human cells, and suggest that other EBLs present within vertebrate genomes may also possess important cellular functions

Antiproliferative Effects of Fluoxetine on Colon Cancer Cells and in a Colonic Carcinogen Mouse Model

The antidepressant fluoxetine has been under discussion because of its potential influence on cancer risk. It was found to inhibit the development of carcinogen-induced preneoplastic lesions in colon tissue, but the mechanisms of action are not well understood. Therefore, we investigated anti-proliferative effects, and used HT29 colon tumor cells in vitro, as well as C57BL/6 mice exposed to intra-rectal treatment with the carcinogen N-methyl-N’-nitro-N-nitrosoguanidine (MNNG) as models. Fluoxetine increased the percentage of HT29 cells in the G0/G1 phase of cell-cycle, and the expression of p27 protein. This was not related to an induction of apoptosis, reactive oxygen species or DNA damage. In vivo, fluoxetine reduced the development of MNNG-induced dysplasia and vascularization-related dysplasia in colon tissue, which was analyzed by histopathological techniques. An anti-proliferative potential of fluoxetine was observed in epithelial and stromal areas. It was accompanied by a reduction of VEGF expression and of the number of cells with angiogenic potential, such as CD133, CD34, and CD31-positive cell clusters. Taken together, our findings suggest that fluoxetine treatment targets steps of early colon carcinogenesis. This confirms its protective potential, explaining at least partially the lower colon cancer risk under antidepressant therapy

Strategic traits of bacteria and archaea vary widely within substrate-use groups.

Quantitative traits such as maximum growth rate and cell radial diameter are one facet of ecological strategy variation across bacteria and archaea. Another facet is substrate-use pathways, such as iron reduction or methylotrophy. Here, we ask how these two facets intersect, using a large compilation of data for culturable species and examining seven quantitative traits (genome size, signal transduction protein count, histidine kinase count, growth temperature, temperature-adjusted maximum growth rate, cell radial diameter and 16S rRNA operon copy number). Overall, quantitative trait variation within groups of organisms possessing a particular substrate-use pathway was very broad, outweighing differences between substrate-use groups. Although some substrate-use groups had significantly different means for some quantitative traits, standard deviation of quantitative trait values within each substrate-use pathway mostly averaged between 1.6 and 1.8 times larger than standard deviation across group means. Most likely, this wide variation reflects ecological strategy: for example, fast maximum growth rate is likely to express an early successional or copiotrophic strategy, and maximum growth varies widely within most substrate-use pathways. In general, it appears that these quantitative traits express different and complementary information about ecological strategy, compared with substrate use

Domain Adaptation Transfer Learning by Kernel Representation Adaptation

International audienceDomain adaptation, where no labeled target data is available, is a challenging task. To solve this problem, we first propose a new SVM based approach with a supplementary MaximumMean Discrepancy (MMD)-like constraint. With this heuristic, source and target data are projected onto a common subspace of a Reproducing Kernel Hilbert Space (RKHS) where both data distributions are expected to become similar. Therefore, a classifier trained on source data might perform well on target data, if the conditional probabilities of labels are similar for source and target data, which is the main assumption of this paper. We demonstrate that adding this constraint does not change the quadratic nature of the optimization problem, so we can use common quadratic optimization tools. Secondly, using the same idea that rendering source and target data similar might ensure efficient transfer learning, and with the same assumption, a Kernel Principal Component Analysis (KPCA) based transfer learning method is proposed. Different from the first heuristic, this second method ensures other higher order moments to be aligned in the RKHS, which leads to better performances. Here again, we select MMD as the similarity measure. Then, a linear transformation is also applied to further improve the alignment between source and target data. We finally compare both methods with other transfer learning methods from the literature to show their efficiency on synthetic and real datasets

Gas distribution in a two-compartment model ventilated in high-frequency percussive and pressure-controlled modes

Purpose To demonstrate in a two-compartment heterogeneous mechanical model of the lung how different loads applied to one compartment, while the other is kept constant, would modify gas distribution between the two pathways under high-frequency percussive ventilation (HFPV). Additionally, these results were compared with those generated in the same model by pressure-controlled ventilation (PCV). Methods Analysis was based on a Siemens lung simulator, representing a fixed branch of the system with an elastance equal to 45 cmH2O/L and a resistance of 20 cmH2O/L/s, and a single-compartment lung simulator, representing a variable pathway of the model, presenting three elastic loads varying between 35 and 85 cmH2O/L and three resistive loads varying between 5 and 50 cmH2O/L/s. Each simulator represented one compartment of the model connected to a central airway that was ventilated with either a volumetric diffusive respirator (VDR-4; Percussionaire Corporation, Sandpoint, ID, USA) or a Siemens Servo 900c ventilator. Flow and pressures were measured in each branch of the model under nine conditions representing the combinations of three elastic and three resistive loads (variable branch) while the loads in the other pathway were kept constant. Results HFPV was able to avoid hyperinflation and reduce tidal volume in a bicompartmental heterogeneous lung model. Under HFPV, gas distribution between the two compartments was not constrained by their time constants. PCV yielded gas distribution as determined by the time constant of each compartment. Conclusions HFPV accommodated volume distribution without overinflating compartments with low time constants, thus possibly presenting a potential protective behavior in mechanically heterogeneous lungs

A semiautomated approach to gene discovery through expressed sequence tag data mining: Discovery of new human transporter genes

Identification and functional characterization of the genes in the human genome remain a major challenge. A principal source of publicly available information used for this purpose is the National Center for Biotechnology Information database of expressed sequence tags (dbEST), which contains over 4 million human ESTs. To extract the information buried in this data more effectively, we have developed a semiautomated method to mine dbEST for uncharacterized human genes. Starting with a single protein input sequence, a family of related proteins from all species is compiled. This entire family is then used to mine the human EST database for new gene candidates. Evaluation of putative new gene candidates in the context of a family of characterized proteins provides a framework for inference of the structure and function of the new genes. When applied to a test data set of 28 families within the major facilitator superfamily (MFS) of membrane transporters, our protocol found 73 previously characterized human MFS genes and 43 new MFS gene candidates. Development of this approach provided insights into the problems and pitfalls of automated data mining using public databases