A role for the deubiquitinating enzyme USP28 in control of the DNA-damage response

The Chk2-p53-PUMA pathway is a major regulator of DNA-damage-induced apoptosis in response to double-strand breaks in vivo. Through analysis of 53BP1 complexes we have discovered a new ubiquitin protease, USP28, which regulates this pathway. Using a human cell line that faithfully recapitulated the Chk2-p53-PUMA pathway, we show that USP28 is required to stabilize Chk2 and 53BP1 in response to DNA damage. In this cell line, both USP28 and Chk2 are required for DNA-damage-induced apoptosis, and they accomplish this in part through regulation of the p53 induction of proapoptotic genes like PUMA. Our studies implicate DNA-damage-induced ubiquitination and deubiquitination as a major regulator of the DNA-damage response for Chk2, 53BP1, and a number of other proteins in the DNA-damage checkpoint pathway, including several mediators, such as Mdc1, Claspin, and TopBP1.     

A molecular biological protocol to distinguish potentially human pathogenic Stenotrophomonas maltophilia from plant-associated Stenotrophomonas rhizophila

In recent years, the importance of the Gram-negative bacterium Stenotrophomonas as an opportunistic pathogen as well as in biotechnology has increased. The aim of the present study was to develop new methods for distinguishing between strains closely related to the potentially human pathogenic Stenotrophomonas maltophilia and those closely related to the plant-associated Stenotrophomonas rhizophila. To accomplish this, 58 strains were characterized by 16S rDNA sequencing and amplified ribosomal DNA restriction analysis (ARDRA), and the occurrence of specific functional genes. Based on 16S rDNA sequences, an ARDRA protocol was developed which allowed differentiation between strains of the S. maltophilia and the S. rhizophila group. As it was known that only salt-treated cells of S. rhizophila were able to synthesize the compatible solute glucosylglycerol (GG), the ggpS gene responsible for GG synthesis was used for differentiation between both species and it was confirmed that it only occurred in S. rhizophila strains. As a further genetic marker the smeD gene, which is part of the genes coding for the multidrug efflux pump SmeDEF from S. maltophilia, was used. Based on the results we propose a combination of fingerprinting techniques using the 16S rDNA and the functional genes ggpS and smeD to distinguish both Stenotrophomonas species.     

Interaction of hypohalous acids and heme peroxidases with unsaturated phosphatidylcholines

The formation of chlorohydrins, bromohydrins, and iodohydrins from 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) by the myeloperoxidase-hydrogen peroxide-halide system was evaluated by means of matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry. This approach allows to detect different kinds of the halogenation reaction even in one mass spectrum. Using a mixture of Cl-, Br-, I-, and SCN- at physiological concentrations, a bromination of POPC dominates by the MPO-hydrogen peroxide-halide system. Hypothiocyanite does apparently not react with the double bond of POPC, but increasing amounts of SCN- cause a decrease of the bromohydrin peaks. An interconversion between different hypohalous acids produced by the myeloperoxidase-hydrogen peroxide-halide system determines the pattern of halogenohydrins in POPC. Especially, hypochlorous acid is able to oxidise Br- to hypobromous acid.     

Polymersomes as nanoreactors for preparative biocatalytic applications: current challenges and future perspectives

Bioprocess and Biosystems Engineering - Polymersomes are hollow, spherical vesicles that are surrounded by a polymer membrane. The applied polymer must be amphiphilic to promote self-assembly in...     

Fast and scalable neural embedding models for biomedical sentence classification

Background

Biomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse semantic and rhetoric categories (e.g., Background, Methods, Results, Conclusions) have been devised, with recent state-of-the-art results reported for a complex deep learning model. Recent evidence showed that shallow and wide neural models such as fastText can provide results that are competitive or superior to complex deep learning models while requiring drastically lower training times and having better scalability. We analyze the efficacy of the fastText model in the classification of biomedical sentences in the PubMed 200k RCT benchmark, and introduce a simple pre-processing step that enables the application of fastText on sentence sequences. Furthermore, we explore the utility of two unsupervised pre-training approaches in scenarios where labeled training data are limited.

Results

Our fastText-based methodology yields a state-of-the-art F1 score of.917 on the PubMed 200k benchmark when sentence ordering is taken into account, with a training time of only 73 s on standard hardware. Applying fastText on single sentences, without taking sentence ordering into account, yielded an F1 score of.852 (training time 13 s). Unsupervised pre-training of N-gram vectors greatly improved the results for small training set sizes, with an increase of F1 score of.21 to.74 when trained on only 1000 randomly picked sentences without taking sentence ordering into account.

Conclusions

Because of it’s ease of use and performance, fastText should be among the first choices of tools when tackling biomedical text classification problems with large corpora. Unsupervised pre-training of N-gram vectors on domain-specific corpora also makes it possible to apply fastText when labeled training data are limited.

     

Oxygen concentration drives local adaptation in the greenlip abalone (Haliotis laevigata)

Understanding adaptation has become one of the major biological questions especially in the light of rapid environmental changes induced by climate change. Ocean temperatures are rising which triggers massive changes in water chemistry and thereby alters the living environment of all marine organisms. Studying adaptation, however, can be tricky because spatial genetic patterns might also occur due to random effects, for example, genetic drift. Genetic drift is reduced in very large and well‐connected populations, such as in broadcast marine spawning organisms. Here, spatial genetic divergence is likely to be produced by selection. In this issue of Molecular Ecology, Sandoval‐Castillo et al. (2018) investigated patterns of spatial genetic divergence and their association with environmental factors in the greenlip abalone (Haliotis laevigata). This commercially important species of mollusc is a broadcast spawner with large population sizes, rendering genetic drift an unlikely factor in the genetic divergence of wild populations. Sandoval‐Castillo et al. (2018) used a ddRAD genomic approach to test for genetic divergence between sampled populations while also measuring different environmental factors, for example, water temperature and oxygen content. The majority of identified SNPs was putatively neutral and showed only low levels of genetic divergence between field sites. However, 323 candidate adaptive markers were identified that clearly separated the individuals into five different clusters. These genetic clusters correlated with environmental clusters mainly determined by water temperature and (correlated) oxygen concentration. Gene annotation of the candidate SNPs revealed a large proportion of loci being involved in biological processes influenced by oxygen availability. The study by Sandoval‐Castillo et al. (2018) in this issue of Molecular Ecology exemplifies the benefits of combining genomic studies with ecological data. It is a great starting point for more detailed (gene function, physiology) as well as broader (biodiversity) investigations that might help us to better understand adaptation and predict ecosystems' resilience and resistance to environmental disturbances. In addition, this information can be applied to implement optimal conservation regime policies and sustainable harvesting strategies, hopefully protecting biodiversity as well as commercial interests in marine life.     

Streptococcus pneumoniae detects and responds to foreign bacterial peptide fragments in its environment

Streptococcus pneumoniae is an important cause of bacterial meningitis and pneumonia but usually colonizes the human nasopharynx harmlessly. As this niche is simultaneously populated by other bacterial species, we looked for a role and pathway of communication between pneumococci and other species. This paper shows that two proteins of non-encapsulated S. pneumoniae, AliB-like ORF 1 and ORF 2, bind specifically to peptides matching other species resulting in changes in the pneumococci. AliB-like ORF 1 binds specifically peptide SETTFGRDFN, matching 50S ribosomal subunit protein L4 of Enterobacteriaceae, and facilitates upregulation of competence for genetic transformation. AliB-like ORF 2 binds specifically peptides containing sequence FPPQS, matching proteins of Prevotella species common in healthy human nasopharyngeal microbiota. We found that AliB-like ORF 2 mediates the early phase of nasopharyngeal colonization in vivo. The ability of S. pneumoniae to bind and respond to peptides of other bacterial species occupying the same host niche may play a key role in adaptation to its environment and in interspecies communication. These findings reveal a completely new concept of pneumococcal interspecies communication which may have implications for communication between other bacterial species and for future interventional therapeutics.     

Robust Selection of Cancer Survival Signatures from High-Throughput Genomic Data Using Two-Fold Subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org).