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  • Global mapping of protein subcellular location in apicomplexans: the parasite as we’ve never seen it before

    Barylyuk, Konstantin; Koreny, Ludek; Ke, Huiling; Butterworth, Simon; Lassadi, Imen; Mourier, Tobias; Breckels, Lisa; Gatto, Laurent; Pain, Arnab; Lilley, Kathryn; Waller, Ross (Access Microbiology, Microbiology Society, 2019-04-24) [Presentation]
    Apicomplexans are human and animal protozoan pathogens responsible for diseases including malaria, cryptosporidiosis and toxoplasmosis. As obligate intracellular parasites they are highly organised cells with numerous novel and specialised sub-compartments that form the basis of their invasion biology, host defence evasion, and novel metabolic traits. However, our understanding of these cells is highly constrained by our limited knowledge of the locations and functions of most of the cell’s proteome. Even in the best-studied apicomplexans (Plasmodium spp. and Toxoplasma gondii) only a small fraction of proteins’ locations have been experimentally determined, with most assignments based on predictions from orthologues in distant relatives. Moreover, many parasite proteins are annotated as ‘hypotheticals’, for example 4113 of 8121 Toxoplasma proteins, and many are unique to parasites stymying even predictions of location or function by comparative biology. To address this deficit in our basic understanding of the compositional organisation of the apicomplexan cell, we have used a spatial proteomics method called hyper LOPIT to simultaneously capture the steady-state subcellular association of thousands of proteins in the apicomplexan Toxoplasma. These protein atlases reveal: extensive protein association networks throughout the cell providing testable hypotheses of their function; conservation and novelty of compartment proteomes between apicomplexans; differential selective pressures across the different cell compartments; and clear instances of protein relocation from one organelle to a different one during apicomplexan speciation. This new, global view of the organisation of the apicomplexan cell proteome provides a much more complete framework for understanding the mechanisms of function and biology of these cells.
  • Uncovering the dark matter of the metagenome one read at a time

    Dimonaco, Nicholas; Creevey, Chris; Hoehndorf, Robert; Kulmanov, Maxat; Liuwei, Wang; Clare, Amanda; Aubrey, Wayne; Kenobi, Kim (Access Microbiology, Microbiology Society, 2019-04-24) [Poster]
    Contemporary metagenomic annotation methods have proven insufficient in our attempts to better understand the complex environments around us. We call the yet to be annotated part of a metagenome it’s ‘dark matter’. The Gene Ontology (GO) is a hierarchical vocabulary used to describe gene product function and a large collection of curated genes with GO annotations already exists. DeepGO utilises deep learning to build models from these curated genes and gene products to predict GO categories for novel proteins. One of the major problems with metagenomic studies today is the process of assembling the environmental DNA sequences into their original genomes. This is difficult, with chimeric metagenomically assembled genomes being common. To avoid this and the computational and time expense, we have modified DeepGO to perform protein function prediction directly from sequence reads with limited protein coding sequence prediction. Three independent models were trained as the following; The first 50 amino acids of a protein were used for training, The last 50 amino acids were used for training, A phasing window of 50 amino acids was used to train across the entirety of a protein sequence. These models were chosen to learn from the different parts of a protein sequence we are likely to capture from only the short unassembled sequence reads. We compared the three models by producing a mock metagenomic community consisting of 6 model bacterial genomes. We evaluated the functions predicted from the unassembled sequence reads and the protein coding sequences predicted from the assembled metagenome.
  • Comparative Genomics of the Genus Methanohalophilus, Including a Newly Isolated Strain From Kebrit Deep in the Red Sea

    Guan, Yue; Ngugi, David; Vinu, Manikandan; Blom, Jochen; Alam, Intikhab; Guillot, Sylvain; Ferry, James G.; Stingl, Ulrich (Frontiers in Microbiology, Frontiers Media SA, 2019-04-24) [Article]
    Halophilic methanogens play an important role in the carbon cycle in hypersaline environments, but are under-represented in culture collections. In this study, we describe a novel Methanohalophilus strain that was isolated from the sulfide-rich brine-seawater interface of Kebrit Deep in the Red Sea. Based on physiological and phylogenomic features, strain RSK, which is the first methanogenic archaeon to be isolated from a deep hypersaline anoxic brine lake of the Red Sea, represents a novel species of this genus. In order to compare the genetic traits underpinning the adaptations of this genus in diverse hypersaline environments, we sequenced the genome of strain RSK and compared it with genomes of previously isolated and well characterized species in this genus (Methanohalophilus mahii, Methanohalophilus halophilus, Methanohalophilus portucalensis, and Methanohalophilus euhalobius). These analyses revealed a highly conserved genomic core of greater than 93% of annotated genes (1490 genes) containing pathways for methylotrophic methanogenesis, osmoprotection through salt-out strategy, and oxidative stress response, among others. Despite the high degree of genomic conservation, species-specific differences in sulfur and glycogen metabolisms, viral resistance, amino acid, and peptide uptake machineries were also evident. Thus, while Methanohalophilus species are found in diverse extreme environments, each genotype also possesses adaptive traits that are likely relevant in their respective hypersaline habitats.
  • Accelerating Kidney Allocation: Simultaneously Expiring Offers

    Mankowski, Michal A; Kosztowski, Martin; Raghavan, Subramanian; Garonzik-Wang, Jacqueline M; Axelrod, David; Segev, Dorry L; Gentry, Sommer E (American Journal of Transplantation, Wiley, 2019-04-23) [Article]
    Placing non-ideal kidneys quickly might reduce discard. We studied changing kidney allocation to eliminate sequential offers, instead making offers to multiple centers for all non-locally allocated kidneys, so that multiple centers must accept or decline within the same one hour. If more than one center accepted an offer, the kidney would go to the highest-priority accepting candidate. Using 2010 KPSAM-SRTR data, we simulated the allocation of 12,933 kidneys, excluding locally allocated and zero-mismatch kidneys. We assumed that each hour of delay decreased the probability of acceptance by 5%, and that kidneys would be discarded after 20 hours of offers beyond the local level. We simulated offering kidneys simultaneously to small, medium, and large batches of centers. Increasing the batch size increased the percentage of kidneys accepted and shortened allocation times. Going from small to large batches increased the number of kidneys accepted from 10,085 (92%) to 10,802 (98%) for low-KDPI, and from 1,257 (65%) to 1,737 (89%) for high-KDPI kidneys. The average number of offers a center received each week was 10.1 for small batches and 16.8 for large batches. Simultaneously expiring offers might allow faster allocation and decrease the number of discards, while still maintaining an acceptable screening burden. This article is protected by copyright. All rights reserved.
  • Copula-based semiparametric models for spatio-temporal data

    Tang, Yanlin; Wang, Huixia Judy; Sun, Ying; Hering, Amanda S (Biometrics, Wiley, 2019-04-22) [Article]
    The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatio-temporal model for analyzing spatio-temporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatio-temporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and new locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions. This article is protected by copyright. All rights reserved.

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