Recent Submissions

  • Genetic Determinants Associated With in Vivo Survival of Burkholderia cenocepacia in the Caenorhabditis elegans Model

    Wong, Yee-Chin; Abd El Ghany, Moataz; Ghazzali, Raeece N. M.; Yap, Soon-Joo; Hoh, Chee-Choong; Pain, Arnab; Nathan, Sheila (Frontiers Media SA, 2018-05-29)
    A Burkholderia cenocepacia infection usually leads to reduced survival and fatal cepacia syndrome in cystic fibrosis patients. The identification of B. cenocepacia essential genes for in vivo survival is key to designing new anti-infectives therapies. We used the Transposon-Directed Insertion Sequencing (TraDIS) approach to identify genes required for B. cenocepacia survival in the model infection host, Caenorhabditis elegans. A B. cenocepacia J2315 transposon pool of ∼500,000 mutants was used to infect C. elegans. We identified 178 genes as crucial for B. cenocepacia survival in the infected nematode. The majority of these genes code for proteins of unknown function, many of which are encoded by the genomic island BcenGI13, while other gene products are involved in nutrient acquisition, general stress responses and LPS O-antigen biosynthesis. Deletion of the glycosyltransferase gene wbxB and a histone-like nucleoid structuring (H-NS) protein-encoding gene (BCAL0154) reduced bacterial accumulation and attenuated virulence in C. elegans. Further analysis using quantitative RT-PCR indicated that BCAL0154 modulates B. cenocepacia pathogenesis via transcriptional regulation of motility-associated genes including fliC, fliG, flhD, and cheB1. This screen has successfully identified genes required for B. cenocepacia survival within the host-associated environment, many of which are potential targets for developing new antimicrobials.
  • PTK2B/Pyk2 overexpression improves a mouse model of Alzheimer's disease

    Giralt, Albert; de Pins, Benoît; Cifuentes-Díaz, Carmen; López-Molina, Laura; Farah, Amel Thamila; Tible, Marion; Deramecourt, Vincent; Arold, Stefan T.; Ginés, Silvia; Hugon, Jacques; Girault, Jean-Antoine (Elsevier BV, 2018-05-24)
    Pyk2 is a Ca2+-activated non-receptor tyrosine kinase enriched in forebrain neurons and involved in synaptic regulation. Human genetic studies associated PTK2B, the gene coding Pyk2, with risk for Alzheimer's disease (AD). We previously showed that Pyk2 is important for hippocampal function, plasticity, and spine structure. However, its potential role in AD is unknown. To address this question we used human brain samples and 5XFAD mice, an amyloid mouse model of AD expressing mutated human amyloid precursor protein and presenilin1. In the hippocampus of 5XFAD mice and in human AD patients' cortex and hippocampus, Pyk2 total levels were normal. However, Pyk2 Tyr-402 phosphorylation levels, reflecting its autophosphorylation-dependent activity, were reduced in 5XFAD mice at 8 months of age but at 3 months. We crossed these mice with Pyk2−/− mice to generate 5XFAD animals devoid of Pyk2. At 8 months the phenotype of 5XFAD x Pyk2−/− double mutant mice was not different from that of 5XFAD. In contrast, overexpression of Pyk2 in the hippocampus of 5XFAD mice, using adeno-associated virus, rescued autophosphorylated Pyk2 levels and improved synaptic markers and performance in several behavioral tasks. Both Pyk2−/− and 5XFAD mice showed an increase of potentially neurotoxic Src cleavage product, which was rescued by Pyk2 overexpression. Manipulating Pyk2 levels had only minor effects on Aβ plaques, which were slightly decreased in hippocampus CA3 region of double mutant mice and increased following overexpression. Our results show that Pyk2 is not essential for the pathogenic effect of human amyloidogenic mutations in the 5XFAD mouse model. However, the slight decrease in plaque number observed in these mice in the absence of Pyk2 and their increase following Pyk2 overexpression suggest a contribution of this kinase in plaque formation. Importantly, a decreased function of Pyk2 was observed in 5XFAD mice, indicated by its decreased autophosphorylation and associated Src alterations. Overcoming this deficit by Pyk2 overexpression improved the behavioral and molecular phenotype of 5XFAD mice. Thus, our results in a mouse model of AD suggest that Pyk2 impairment may play a role in the symptoms of the disease.
  • In silico exploration of Red Sea Bacillus genomes for natural product biosynthetic gene clusters

    Othoum, Ghofran K; Bougouffa, Salim; Razali, Rozaimi; Bokhari, Ameerah; Alamoudi, Soha; Antunes, André; Gao, Xin; Hoehndorf, Robert; Arold, Stefan T.; Gojobori, Takashi; Hirt, Heribert; Mijakovic, Ivan; Bajic, Vladimir B.; Lafi, Feras Fawzi; Essack, Magbubah (Springer Nature, 2018-05-22)
    BackgroundThe increasing spectrum of multidrug-resistant bacteria is a major global public health concern, necessitating discovery of novel antimicrobial agents. Here, members of the genus Bacillus are investigated as a potentially attractive source of novel antibiotics due to their broad spectrum of antimicrobial activities. We specifically focus on a computational analysis of the distinctive biosynthetic potential of Bacillus paralicheniformis strains isolated from the Red Sea, an ecosystem exposed to adverse, highly saline and hot conditions.ResultsWe report the complete circular and annotated genomes of two Red Sea strains, B. paralicheniformis Bac48 isolated from mangrove mud and B. paralicheniformis Bac84 isolated from microbial mat collected from Rabigh Harbor Lagoon in Saudi Arabia. Comparing the genomes of B. paralicheniformis Bac48 and B. paralicheniformis Bac84 with nine publicly available complete genomes of B. licheniformis and three genomes of B. paralicheniformis, revealed that all of the B. paralicheniformis strains in this study are more enriched in nonribosomal peptides (NRPs). We further report the first computationally identified trans-acyltransferase (trans-AT) nonribosomal peptide synthetase/polyketide synthase (PKS/ NRPS) cluster in strains of this species.ConclusionsB. paralicheniformis species have more genes associated with biosynthesis of antimicrobial bioactive compounds than other previously characterized species of B. licheniformis, which suggests that these species are better potential sources for novel antibiotics. Moreover, the genome of the Red Sea strain B. paralicheniformis Bac48 is more enriched in modular PKS genes compared to B. licheniformis strains and other B. paralicheniformis strains. This may be linked to adaptations that strains surviving in the Red Sea underwent to survive in the relatively hot and saline ecosystems.
  • Endogenous Control Mechanisms of FAK and PYK2 and Their Relevance to Cancer Development and Therapy

    Naser, Rayan Mohammad Mahmoud; Aldehaiman, Abdullah; Diaz Galicia, Miriam Escarlet; Arold, Stefan T. (MDPI AG, 2018-05-10)
    Focal adhesion kinase (FAK) and its close paralogue, proline-rich tyrosine kinase 2 (PYK2), are key regulators of aggressive spreading and metastasis of cancer cells. While targeted small-molecule inhibitors of FAK and PYK2 are showing promising antitumor activity, their clinical long-term efficacy may be undermined by the strong capacity of cancer cells to evade anti-kinase drugs. In healthy cells, the expression and/or function of FAK and PYK2 is tightly controlled through modulation of gene expression, competing alternatively spliced forms, non-coding RNAs, and proteins that directly or indirectly affect kinase activation or protein stability. The molecular factors involved are frequently deregulated in cancer cells. Here, we review the endogenous mechanisms controlling FAK and PYK2, and discuss how these mechanisms could inspire or improve anticancer therapies.
  • SupportNet: a novel incremental learning framework through deep learning and support data

    Li, Yu; Li, Zhongxiao; Ding, Lizhong; Hu, Yuhui; Chen, Wei; Gao, Xin (Cold Spring Harbor Laboratory, 2018-05-08)
    Motivation: In most biological data sets, the amount of data is regularly growing and the number of classes is continuously increasing. To deal with the new data from the new classes, one approach is to train a classification model, e.g., a deep learning model, from scratch based on both old and new data. This approach is highly computationally costly and the extracted features are likely very different from the ones extracted by the model trained on the old data alone, which leads to poor model robustness. Another approach is to fine tune the trained model from the old data on the new data. However, this approach often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as the catastrophic forgetting problem. To our knowledge, this problem has not been studied in the field of bioinformatics despite its existence in many bioinformatic problems. Results: Here we propose a novel method, SupportNet, to solve the catastrophic forgetting problem efficiently and effectively. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to ensure the robustness of the learned model. Comprehensive experiments on various tasks, including enzyme function prediction, subcellular structure classification and breast tumor classification, show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and reaches similar performance as the deep learning model trained from scratch on both old and new data. Availability: Our program is accessible at: \url{https://github.com/lykaust15/SupportNet}.
  • DeepPVP: phenotype-based prioritization of causative variants using deep learning

    Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-05-02)
    Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
  • OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants

    Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-05-02)
    Purpose: An increasing number of Mendelian disorders have been identified for which two or more variants in one or more genes are required to cause the disease, or significantly modify its severity or phenotype. It is difficult to discover such interactions using existing approaches. The purpose of our work is to develop and evaluate a system that can identify combinations of variants underlying oligogenic diseases in individual whole exome or whole genome sequences. Methods: Information that links patient phenotypes to databases of gene-phenotype associations observed in clinical research can provide useful information and improve variant prioritization for Mendelian diseases. Additionally, background knowledge about interactions between genes can be utilized to guide and restrict the selection of candidate disease modules. Results: We developed OligoPVP, an algorithm that can be used to identify variants in oligogenic diseases and their interactions, using whole exome or whole genome sequences together with patient phenotypes as input. We demonstrate that OligoPVP has significantly improved performance when compared to state of the art pathogenicity detection methods. Conclusions: Our results show that OligoPVP can efficiently detect oligogenic interactions using a phenotype-driven approach and identify etiologically important variants in whole genomes.
  • Metagenome-based diversity analyses suggest a strong locality signal for bacterial communities associated with oyster aquaculture farms in Ofunato Bay

    Kobiyama, Atsushi; Ikeo, Kazuho; Reza, Md. Shaheed; Rashid, Jonaira; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Sato, Shigeru; Ogata, Takehiko; Jimbo, Mitsuru; Kudo, Toshiaki; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo (Elsevier BV, 2018-04-30)
    Ofunato Bay, in Japan, is the home of buoy-and-rope-type oyster aquaculture activities. Since the oysters filter suspended materials and excrete organic matter into the seawater, bacterial communities residing in its vicinity may show dynamic changes depending on the oyster culture activities. We employed a shotgun metagenomic technique to study bacterial communities near oyster aquaculture facilities at the center of the bay (KSt. 2) and compared the results with those of two other localities far from the station, one to the northeast (innermost bay, KSt. 1) and the other to the southwest (bay entrance, KSt. 3). Seawater samples were collected every month from January to December 2015 from the surface (1 m) and deeper (8 or 10 m) layers of the three locations, and the sequentially filtered fraction on 0.2-μm membranes was sequenced on an Illumina MiSeq system. The acquired reads were uploaded to MG-RAST for KEGG functional abundance analysis, while taxonomic analyses at the phylum and genus levels were performed using MEGAN after parsing the BLAST output. Discrimination analyses were then performed using the ROC-AUC value of the cross validation, targeting the depth (shallow or deep), locality [(KSt. 1 + KSt. 2) vs. KSt 3; (KSt. 1 + KSt. 3) vs. KSt. 2 or the (KSt. 2 + KSt. 3) vs. KSt. 1] and seasonality (12 months). The matrix discrimination analysis on the adjacent 2 continuous seasons by ROC-AUC, which was based on the datasets that originated from different depths, localities and months, showed the strongest discrimination signal on the taxonomy matrix at the phylum level for the datasets from July to August compared with those from September to June, while the KEGG matrix showed the strongest signal for the datasets from March to June compared with those from July to February. Then, the locality combination was subjected to the same ROC-AUC discrimination analysis, resulting in significant differences between KSt. 2 and KSt. 1 + KSt. 3 on the KEGG matrix. These results suggest that aquaculture activities markedly affect bacterial functions.
  • Seasonal changes in the communities of photosynthetic picoeukaryotes in Ofunato Bay as revealed by shotgun metagenomic sequencing

    Rashid, Jonaira; Kobiyama, Atsushi; Reza, Md. Shaheed; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Ikeo, Kazuho; Sato, Shigeru; Ogata, Takehiko; Kudo, Toshiaki; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo (Elsevier BV, 2018-04-30)
    Small photosynthetic eukaryotes play important roles in oceanic food webs in coastal regions. We investigated seasonal changes in the communities of photosynthetic picoeukaryotes (PPEs) of the class Mamiellophyceae, including the genera Bathycoccus, Micromonas and Ostreococcus, in Ofunato Bay, which is located in northeastern Japan and faces the Pacific Ocean. The abundances of PPEs were assessed over a period of one year in 2015 at three sampling stations, KSt. 1 (innermost bay area), KSt. 2 (middle bay area) and KSt. 3 (bay entrance area) at depths of 1 m (KSt. 1, KSt. 2 and KSt. 3), 8 m (KSt. 1) or 10 m (KSt. 2 and KSt. 3) by employing MiSeq shotgun metagenomic sequencing. The total abundances of Bathycoccus, Ostreococcus and Micromonas were in the ranges of 42–49%, 35–49% and 13–17%, respectively. Considering all assayed sampling stations and depths, seasonal changes revealed high abundances of PPEs during the winter and summer and low abundances during late winter to early spring and late summer to early autumn. Bathycoccus was most abundant in the winter, and Ostreococcus showed a high abundance during the summer. Another genus, Micromonas, was relatively low in abundance throughout the study period. Taken together with previously suggested blooming periods of phytoplankton, as revealed by chlorophyll a concentrations in Ofunato Bay during spring and late autumn, these results for PPEs suggest that greater phytoplankton blooming has a negative influence on the seasonal occurrences of PPEs in the bay.
  • Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

    AlShahrani, Mona; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-04-30)
    In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results: We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.
  • Taxonomic profiles in metagenomic analyses of free-living microbial communities in the Ofunato Bay

    Reza, Md. Shaheed; Kobiyama, Atsushi; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Ikeo, Kazuho; Sato, Shigeru; Ogata, Takehiko; Jimbo, Mitsuru; Kudo, Toshiaki; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo (Elsevier BV, 2018-04-27)
    The Ofunato Bay in Iwate Prefecture, Japan is a deep coastal bay located at the center of the Sanriku Rias coast and considered an economically and environmentally important asset. Here, we describe the first whole genome sequencing (WGS) study on the microbial community of the bay, where surface water samples were collected from three stations along its length to cover the entire bay; we preliminarily sequenced a 0.2 μm filter fraction among sequentially size-fractionated samples of 20.0, 5.0, 0.8 and 0.2 μm filters, targeting the free-living fraction only. From the 0.27–0.34 Gb WGS library, 0.9 × 106–1.2 × 106 reads from three sampling stations revealed 29 bacterial phyla (~80% of assigned reads), 3 archaeal phyla (~4%) and 59 eukaryotic phyla (~15%). Microbial diversity obtained from the WGS approach was compared with 16S rRNA gene results by mining WGS metagenomes, and we found similar estimates. The most frequently recovered bacterial sequences were Proteobacteria, predominantly comprised of 18.0–19.6% Planktomarina (Family Rhodobacteraceae) and 13.7–17.5% Candidatus Pelagibacter (Family Pelagibacterales). Other dominant bacterial genera, including Polaribacter (3.5–6.1%), Flavobacterium (1.8–2.6%), Sphingobacterium (1.4–1.6%) and Cellulophaga (1.4–2.0%), were members of Bacteroidetes and likely associated with the degradation and turnover of organic matter. The Marine Group I Archaea Nitrosopumilus was also detected. Remarkably, eukaryotic green alga Bathycoccus, Ostreococcus and Micromonas accounted for 8.8–15.2%, 3.6–4.9% and 2.1–3.1% of total read counts, respectively, highlighting their potential roles in the phytoplankton bloom after winter mixing.
  • Seasonal changes in the abundance of bacterial genes related to dimethylsulfoniopropionate catabolism in seawater from Ofunato Bay revealed by metagenomic analysis

    Kudo, Toshiaki; Kobiyama, Atsushi; Rashid, Jonaira; Reza, Shaheed; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Ikeo, Kazuho; Sato, Shigeru; Ogata, Takehiko; Jimbo, Mitsuru; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Segawa, Satoshi; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo (Elsevier BV, 2018-04-26)
    Ofunato Bay is located in the northeastern Pacific Ocean area of Japan, and it has the highest biodiversity of marine organisms in the world, primarily due to tidal influences from the cold Oyashio and warm Kuroshio currents. Our previous results from performing shotgun metagenomics indicated that Candidatus Pelagibacter ubique and Planktomarina temperata were the dominant bacteria (Reza et al., 2018a, 2018b). These bacteria are reportedly able to catabolize dimethylsulfoniopropionate (DMSP) produced from phytoplankton into dimethyl sulfide (DMS) or methanethiol (MeSH). This study was focused on seasonal changes in the abundances of bacterial genes (dddP, dmdA) related to DMSP catabolism in the seawater of Ofunato Bay by BLAST+ analysis using shotgun metagenomic datasets. We found seasonal changes among the Candidatus Pelagibacter ubique strains, including those of the HTCC1062 type and the Red Sea type. A good correlation was observed between the chlorophyll a concentrations and the abundances of the catabolic genes, suggesting that the bacteria directly interact with phytoplankton in the marine material cycle system and play important roles in producing DMS and MeSH from DMSP as signaling molecules for the possible formation of the scent of the tidewater or as fish attractants.
  • Basin-scale seasonal changes in marine free-living bacterioplankton community in the Ofunato Bay

    Reza, Md. Shaheed; Kobiyama, Atsushi; Yamada, Yuichiro; Ikeda, Yuri; Ikeda, Daisuke; Mizusawa, Nanami; Ikeo, Kazuho; Sato, Shigeru; Ogata, Takehiko; Jimbo, Mitsuru; Kudo, Toshiaki; Kaga, Shinnosuke; Watanabe, Shiho; Naiki, Kimiaki; Kaga, Yoshimasa; Mineta, Katsuhiko; Bajic, Vladimir B.; Gojobori, Takashi; Watabe, Shugo (Elsevier BV, 2018-04-26)
    The Ofunato Bay in the northeastern Pacific Ocean area of Japan possesses the highest biodiversity of marine organisms in the world and has attracted much attention due to its economic and environmental importance. We report here a shotgun metagenomic analysis of the year-round variation in free-living bacterioplankton collected across the entire length of the bay. Phylogenetic differences among spring, summer, autumn and winter bacterioplankton suggested that members of Proteobacteria tended to decrease at high water temperatures and increase at low temperatures. It was revealed that Candidatus Pelagibacter varied seasonally, reaching as much as 60% of all sequences at the genus level in the surface waters during winter. This increase was more evident in the deeper waters, where they reached up to 75%. The relative abundance of Planktomarina also rose during winter and fell during summer. A significant component of the winter bacterioplankton community was Archaea (mainly represented by Nitrosopumilus), as their relative abundance was very low during spring and summer but high during winter. In contrast, Actinobacteria and Cyanobacteria appeared to be higher in abundance during high-temperature periods. It was also revealed that Bacteroidetes constituted a significant component of the summer bacterioplankton community, being the second largest bacterial phylum detected in the Ofunato Bay. Its members, notably Polaribacter and Flavobacterium, were found to be high in abundance during spring and summer, particularly in the surface waters. Principal component analysis and hierarchal clustering analyses showed that the bacterial communities in the Ofunato Bay changed seasonally, likely caused by the levels of organic matter, which would be deeply mixed with surface runoff in the winter.
  • RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

    Kim, Ji-Sung; Gao, Xin; Rzhetsky, Andrey (Public Library of Science (PLoS), 2018-04-26)
    Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, as race and ethnicity are powerful confounders for many health exposures and treatment outcomes; race and ethnicity are closely linked to population-specific genetic variation. We showed that deep neural networks generate more accurate estimates for missing racial and ethnic information than competing methods (e.g., logistic regression, random forest, support vector machines, and gradient-boosted decision trees). RIDDLE yielded significantly better classification performance across all metrics that were considered: accuracy, cross-entropy loss (error), precision, recall, and area under the curve for receiver operating characteristic plots (all p < 10-9). We made specific efforts to interpret the trained neural network models to identify, quantify, and visualize medical features which are predictive of race and ethnicity. We used these characterizations of informative features to perform a systematic comparison of differential disease patterns by race and ethnicity. The fact that clinical histories are informative for imputing race and ethnicity could reflect (1) a skewed distribution of blue- and white-collar professions across racial and ethnic groups, (2) uneven accessibility and subjective importance of prophylactic health, (3) possible variation in lifestyle, such as dietary habits, and (4) differences in background genetic variation which predispose to diseases.
  • The epigenetic landscape of transgenerational acclimation to ocean warming

    Ryu, Tae Woo; Veilleux, Heather D.; Donelson, Jennifer M.; Munday, Philip L.; Ravasi, Timothy (Springer Nature, 2018-04-26)
    Epigenetic inheritance is a potential mechanism by which the environment in one generation can influence the performance of future generations1. Rapid climate change threatens the survival of many organisms; however, recent studies show that some species can adjust to climate-related stress when both parents and their offspring experience the same environmental change2,3. Whether such transgenerational acclimation could have an epigenetic basis is unknown. Here, by sequencing the liver genome, methylomes and transcriptomes of the coral reef fish, Acanthochromis polyacanthus, exposed to current day (+0 °C) or future ocean temperatures (+3 °C) for one generation, two generations and incrementally across generations, we identified 2,467 differentially methylated regions (DMRs) and 1,870 associated genes that respond to higher temperatures within and between generations. Of these genes, 193 were significantly correlated to the transgenerationally acclimating phenotypic trait, aerobic scope, with functions in insulin response, energy homeostasis, mitochondrial activity, oxygen consumption and angiogenesis. These genes may therefore play a key role in restoring performance across generations in fish exposed to increased temperatures associated with climate change. Our study is the first to demonstrate a possible association between DNA methylation and transgenerational acclimation to climate change in a vertebrate.
  • Genome Reduction in Psychromonas Species within the Gut of an Amphipod from the Ocean’s Deepest Point

    Zhang, Weipeng; Tian, Ren-Mao; Sun, Jin; Bougouffa, Salim; Ding, Wei; Cai, Lin; Lan, Yi; Tong, Haoya; Li, Yongxin; Jamieson, Alan J.; Bajic, Vladimir B.; Drazen, Jeffrey C.; Bartlett, Douglas; Qian, Pei-Yuan (American Society for Microbiology, 2018-04-25)
    Amphipods are the dominant scavenging metazoan species in the Mariana Trench, the deepest known point in Earth's oceans. Here the gut microbiota of the amphipod Hirondellea gigas collected from the Challenger and Sirena Deeps of the Mariana Trench were investigated. The 11 amphipod individuals included for analyses were dominated by Psychromonas, of which a nearly complete genome was successfully recovered (designated CDP1). Compared with previously reported free-living Psychromonas strains, CDP1 has a highly reduced genome. Genome alignment showed deletion of the trimethylamine N-oxide (TMAO) reducing gene cluster in CDP1, suggesting that the
  • Transcriptional landscape of Mycobacterium tuberculosis infection in macrophages

    Roy, Sugata; Schmeier, Sebastian; Kaczkowski, Bogumil; Arner, Erik; Alam, Tanvir; Ozturk, Mumin; Tamgue, Ousman; Parihar, Suraj P.; Kawaji, Hideya; Itoh, Masayoshi; Lassmann, Timo; Carninci, Piero; Hayashizaki, Yoshihide; Forrest, Alistair R. R.; Guler, Reto; Bajic, Vladimir B.; Brombacher, Frank; Suzuki, Harukazu (Springer Nature, 2018-04-24)
    Mycobacterium tuberculosis (Mtb) infection reveals complex and dynamic host-pathogen interactions, leading to host protection or pathogenesis. Using a unique transcriptome technology (CAGE), we investigated the promoter-based transcriptional landscape of IFNγ (M1) or IL-4/IL-13 (M2) stimulated macrophages during Mtb infection in a time-kinetic manner. Mtb infection widely and drastically altered macrophage-specific gene expression, which is far larger than that of M1 or M2 activations. Gene Ontology enrichment analysis for Mtb-induced differentially expressed genes revealed various terms, related to host-protection and inflammation, enriched in up-regulated genes. On the other hand, terms related to dis-regulation of cellular functions were enriched in down-regulated genes. Differential expression analysis revealed known as well as novel transcription factor genes in Mtb infection, many of them significantly down-regulated. IFNγ or IL-4/IL-13 pre-stimulation induce additional differentially expressed genes in Mtb-infected macrophages. Cluster analysis uncovered significant numbers, prolonging their expressional changes. Furthermore, Mtb infection augmented cytokine-mediated M1 and M2 pre-activations. In addition, we identified unique transcriptional features of Mtb-mediated differentially expressed lncRNAs. In summary we provide a comprehensive in depth gene expression/regulation profile in Mtb-infected macrophages, an important step forward for a better understanding of host-pathogen interaction dynamics in Mtb infection.
  • 1057 Vemurafenib acts as an aryl hydrocarbon receptor antagonist

    Hawerkamp, H.C.; Kislat, A.; Gerber, P.; Pollet, M.; Soshilov, A.A.; Denison, M.S.; Momin, A.A.; Arold, Stefan T.; Datsi, A.; Braun, S.A.; Lacouture, M.E.; Haarmann-Stemmann, T.; Homey, B.; Meller, S. (Elsevier BV, 2018-04-19)
  • 665 Nail lesions in 30 old inbred mouse strains

    Linn, S.C.; Mustonen, A.M.; Silva, K.A.; Kennedy, V.E.; Sundberg, B.A.; Bechtold, L.S.; Cusolito, L.R.; Alghamdi, S.; Hoehndorf, Robert; Schofield, P.N.; Sundberg, J.P. (Elsevier BV, 2018-04-19)
  • Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

    Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M (Oxford University Press (OUP), 2018-04-10)
    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

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