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1.
Plant Cell Environ. 2018 Aug;41(8):1935-1947. doi: 10.1111/pce.13353. Epub 2018 Jun 19.

Whole-transcriptome analysis reveals genetic factors underlying flowering time regulation in rapeseed (Brassica napus L.).

Author information

1
Plant Breeding Institute, Christian-Albrechts-University of Kiel, Kiel, Germany.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
3
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
4
Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany.

Abstract

Rapeseed (Brassica napus L.), one of the most important sources of vegetable oil and protein-rich meals worldwide, is adapted to different geographical regions by modification of flowering time. Rapeseed cultivars have different day length and vernalization requirements, which categorize them into winter, spring, and semiwinter ecotypes. To gain a deeper insight into genetic factors controlling floral transition in B. napus, we performed RNA sequencing (RNA-seq) in the semiwinter doubled haploid line, Ningyou7, at different developmental stages and temperature regimes. The expression profiles of more than 54,000 gene models were compared between different treatments and developmental stages, and the differentially expressed genes were considered as targets for association analysis and genetic mapping to confirm their role in floral transition. Consequently, 36 genes with association to flowering time, seed yield, or both were identified. We found novel indications for neofunctionalization in homologs of known flowering time regulators like VIN3 and FUL. Our study proved the potential of RNA-seq along with association analysis and genetic mapping to identify candidate genes for floral transition in rapeseed. The candidate genes identified in this study could be subjected to genetic modification or targeted mutagenesis and genotype building to breed rapeseed adapted to certain environments.

KEYWORDS:

RNA-seq; association analysis; differentially expressed genes; genetic mapping; pleiotropic effects; vernalization; yield

PMID:
29813173
DOI:
10.1111/pce.13353
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2.
MBio. 2018 Apr 24;9(2). pii: e00419-18. doi: 10.1128/mBio.00419-18.

A Viral Suppressor Modulates the Plant Immune Response Early in Infection by Regulating MicroRNA Activity.

Author information

1
Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle/Saale, Germany.
2
Institute for Sustainable Plant Protection-Consiglio Nazionale delle Ricerche, Turin, Italy.
3
Institute of Informatics, Martin Luther University Halle-Wittenberg, Halle/Saale, Germany.
4
Institute for Sustainable Plant Protection-Consiglio Nazionale delle Ricerche, Research Unit of Bari, Bari, Italy.
5
Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle/Saale, Germany sven.behrens@biochemtech.uni-halle.de.
#
Contributed equally

Abstract

Many viral suppressors (VSRs) counteract antiviral RNA silencing, a central component of the plant's immune response by sequestration of virus-derived antiviral small interfering RNAs (siRNAs). Here, we addressed how VSRs affect the activities of cellular microRNAs (miRNAs) during a viral infection by characterizing the interactions of two unrelated VSRs, the Tombusvirus p19 and the Cucumovirus 2b, with miRNA 162 (miR162), miR168, and miR403. These miRNAs regulate the expression of the important silencing factors Dicer-like protein 1 (DCL1) and Argonaute proteins 1 and 2 (AGO1 and AGO2), respectively. Interestingly, while the two VSRs showed similar binding profiles, the miRNAs were bound with significantly different affinities, for example, with the affinity of miR162 greatly exceeding that of miR168. In vitro silencing experiments revealed that p19 and 2b affect miRNA-mediated silencing of the DCL1, AGO1, and AGO2 mRNAs in strict accordance with the VSR's miRNA-binding profiles. In Tombusvirus-infected plants, the miRNA-binding behavior of p19 closely corresponded to that in vitro Most importantly, in contrast to controls with a Δp19 virus, infections with wild-type (wt) virus led to changes of the levels of the miRNA-targeted mRNAs, and these changes correlated with the miRNA-binding preferences of p19. This was observed exclusively in the early stage of infection when viral genomes are proposed to be susceptible to silencing and viral siRNA (vsiRNA) concentrations are low. Accordingly, our study suggests that differential binding of miRNAs by VSRs is a widespread viral mechanism to coordinately modulate cellular gene expression and the antiviral immune response during infection initiation.IMPORTANCE Plant viruses manipulate their hosts in various ways. Viral suppressor proteins (VSRs) interfere with the plant's immune response by sequestering small, antivirally acting vsiRNAs, which are processed from viral RNAs during the plant's RNA-silencing response. Here, we examined the effects of VSRs on cellular microRNAs (miRNAs), which show a high degree of similarity with vsiRNAs. Binding experiments with two unrelated VSRs and three important regulatory miRNAs revealed that the proteins exhibit similar miRNA-binding profiles but bind different miRNAs at considerably different affinities. Most interestingly, experiments in plants showed that in the early infection phase, the Tombusvirus VSR p19 modulates the activity of these miRNAs on their target mRNAs very differently and that this differential regulation strictly correlates with the binding affinities of p19 for the respective miRNAs. Our data suggest that VSRs may specifically control plant gene expression and the early immune response by differential sequestration of miRNAs.

KEYWORDS:

RISC; RNA interference; RNA replication; RNA silencing; RNA-protein interactions; VSR; antiviral; immune evasion; miRNA; plant viruses; plus-strand RNA virus; siRNA

3.
Proc Biol Sci. 2018 Apr 25;285(1877). pii: 20172806. doi: 10.1098/rspb.2017.2806.

Genome-wide single nucleotide polymorphism scan suggests adaptation to urbanization in an important pollinator, the red-tailed bumblebee (Bombus lapidarius L.).

Author information

1
General Zoology, Institute of Biology, Martin Luther University Halle-Wittenberg, Hoher Weg 8, 06120 Halle (Saale), Germany panatheod@gmail.com.
2
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
3
General Zoology, Institute of Biology, Martin Luther University Halle-Wittenberg, Hoher Weg 8, 06120 Halle (Saale), Germany.
4
Molecular Evolution and Animal Systematics, Institute of Biology, University of Leipzig, Talstrasse 33, 04103 Leipzig, Germany.
5
Life Sciences Center, Vilnius University, Saulėtekio al. 7, 10257 Vilnius, Lithuania.
6
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany.

Abstract

Urbanization is considered a global threat to biodiversity; the growth of cities results in an increase in impervious surfaces, soil and air pollution, fragmentation of natural vegetation and invasion of non-native species, along with numerous environmental changes, including the heat island phenomenon. The combination of these effects constitutes a challenge for both the survival and persistence of many native species, while also imposing altered selective regimes. Here, using 110 314 single nucleotide polymorphisms generated by restriction-site-associated DNA sequencing, we investigated the genome-wide effects of urbanization on putative neutral and adaptive genomic diversity in a major insect pollinator, Bombus lapidarius, collected from nine German cities and nine paired rural sites. Overall, genetic differentiation among sites was low and there was no obvious genome-wide genetic structuring, suggesting the absence of strong effects of urbanization on gene flow. We nevertheless identified several loci under directional selection, a subset of which was associated with urban land use, including the percentage of impervious surface surrounding each sampling site. Overall, our results provide evidence of local adaptation to urbanization in the face of gene flow in a highly mobile insect pollinator.

KEYWORDS:

RAD-seq; genotype–environment association; landscape genomics; local adaptation; panmixia; urban evolutionary biology

PMID:
29669900
PMCID:
PMC5936727
[Available on 2019-04-25]
DOI:
10.1098/rspb.2017.2806
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4.
Proc Natl Acad Sci U S A. 2018 Mar 6;115(10):E2447-E2456. doi: 10.1073/pnas.1718263115. Epub 2018 Feb 13.

Transcriptome dynamics at Arabidopsis graft junctions reveal an intertissue recognition mechanism that activates vascular regeneration.

Author information

1
Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom; charles.melnyk@slu.se.
2
Department of Plant Biology, Swedish University of Agricultural Sciences, 756 51 Uppsala, Sweden.
3
Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.
5
Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom.
6
Institute of Plant Science, University of Bern, 3013 Bern, Switzerland.
7
Graduate School of Biological Sciences, Nara Institute of Science and Technology, 630-0192 Ikoma, Japan.
8
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany.
9
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125.
10
Howard Hughes Medical Institute, California Institute of Technology, Pasadena, CA 91125.

Abstract

The ability for cut tissues to join and form a chimeric organism is a remarkable property of many plants; however, grafting is poorly characterized at the molecular level. To better understand this process, we monitored genome-wide gene expression changes in grafted Arabidopsis thaliana hypocotyls. We observed a sequential activation of genes associated with cambium, phloem, and xylem formation. Tissues above and below the graft rapidly developed an asymmetry such that many genes were more highly expressed on one side than on the other. This asymmetry correlated with sugar-responsive genes, and we observed an accumulation of starch above the graft junction. This accumulation decreased along with asymmetry once the sugar-transporting vascular tissues reconnected. Despite the initial starvation response below the graft, many genes associated with vascular formation were rapidly activated in grafted tissues but not in cut and separated tissues, indicating that a recognition mechanism was activated independently of functional vascular connections. Auxin, which is transported cell to cell, had a rapidly elevated response that was symmetric, suggesting that auxin was perceived by the root within hours of tissue attachment to activate the vascular regeneration process. A subset of genes was expressed only in grafted tissues, indicating that wound healing proceeded via different mechanisms depending on the presence or absence of adjoining tissues. Such a recognition process could have broader relevance for tissue regeneration, intertissue communication, and tissue fusion events.

KEYWORDS:

auxin; plant grafting; regeneration; vascular tissue; wound healing

5.
Bioinformatics. 2018 May 1;34(9):1589-1590. doi: 10.1093/bioinformatics/btx835.

myTAI: evolutionary transcriptomics with R.

Author information

1
Sainsbury Laboratory Cambridge, University of Cambridge, Cambridge CB2 1LR, UK.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.
3
Université de Lausanne, Département d'Ecologie et d'Evolution, Quartier Sorge, 1015 Lausanne, Switzerland.
4
Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.
5
German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany.

Abstract

Motivation:

Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes.

Results:

The myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner.

Availability and implementation:

The open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html.

Contact:

hgd23@cam.ac.uk.

Supplementary information:

Supplementary data are available at Bioinformatics online.

6.
Genome Biol. 2017 Oct 31;18(1):204. doi: 10.1186/s13059-017-1332-x.

Adaptation of iCLIP to plants determines the binding landscape of the clock-regulated RNA-binding protein AtGRP7.

Author information

1
RNA Biology and Molecular Physiology, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
4
RNA Biology and Molecular Physiology, Faculty of Biology, Bielefeld University, Bielefeld, Germany. dorothee.staiger@uni-bielefeld.de.

Abstract

BACKGROUND:

Functions for RNA-binding proteins in orchestrating plant development and environmental responses are well established. However, the lack of a genome-wide view of their in vivo binding targets and binding landscapes represents a gap in understanding the mode of action of plant RNA-binding proteins. Here, we adapt individual nucleotide resolution crosslinking and immunoprecipitation (iCLIP) genome-wide to determine the binding repertoire of the circadian clock-regulated Arabidopsis thaliana glycine-rich RNA-binding protein AtGRP7.

RESULTS:

iCLIP identifies 858 transcripts with significantly enriched crosslink sites in plants expressing AtGRP7-GFP that are absent in plants expressing an RNA-binding-dead AtGRP7 variant or GFP alone. To independently validate the targets, we performed RNA immunoprecipitation (RIP)-sequencing of AtGRP7-GFP plants subjected to formaldehyde fixation. Of the iCLIP targets, 452 were also identified by RIP-seq and represent a set of high-confidence binders. AtGRP7 can bind to all transcript regions, with a preference for 3' untranslated regions. In the vicinity of crosslink sites, U/C-rich motifs are overrepresented. Cross-referencing the targets against transcriptome changes in AtGRP7 loss-of-function mutants or AtGRP7-overexpressing plants reveals a predominantly negative effect of AtGRP7 on its targets. In particular, elevated AtGRP7 levels lead to damping of circadian oscillations of transcripts, including DORMANCY/AUXIN ASSOCIATED FAMILY PROTEIN2 and CCR-LIKE. Furthermore, several targets show changes in alternative splicing or polyadenylation in response to altered AtGRP7 levels.

CONCLUSIONS:

We have established iCLIP for plants to identify target transcripts of the RNA-binding protein AtGRP7. This paves the way to investigate the dynamics of posttranscriptional networks in response to exogenous and endogenous cues.

KEYWORDS:

Circadian rhythm; Individual nucleotide resolution crosslinking and immunoprecipitation (iCLIP); RNA immunoprecipitation (RIP); RNA-binding protein

PMID:
29084609
PMCID:
PMC5663106
DOI:
10.1186/s13059-017-1332-x
[Indexed for MEDLINE]
Free PMC Article
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7.
Ecol Lett. 2017 Dec;20(12):1576-1590. doi: 10.1111/ele.12858. Epub 2017 Oct 12.

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward.

Author information

1
Department of Integrative Biology, University of South Florida, Tampa, FL, 33620, USA.
2
Estación Biológica de Doñana, CSIC, 41092, Sevilla, Spain.
3
Gregor Mendel Institute of Molecular Plant Biology, 1030, Vienna, Austrian Academy of Sciences, Vienna Biocenter (VBC), Austria.
4
Plant Evolutionary Ecology, University of Tübingen, 72076, Tübingen, Germany.
5
Institut de Recherche en Horticulture et Semences, 49071, Beaucouzé Cedex, France.
6
European Research Institute for the Biology of Ageing, University Medical Center Groningen, 9713, Groningen, The Netherlands.
7
Institute of Computational Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
8
School of Life Sciences Weihenstephan, Technical University of Munich, 85354, Freising, Germany.
9
Department of Community Ecology, Helmholtz Centre for Environmental Research - UFZ, 06120, Halle, Germany.
10
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103, Leipzig, Germany.
11
Institut für Informatik, University of Leipzig, 04107, Leipzig, Germany.
12
Institute of Computer Science, University of Halle, 06120, Halle, Germany.
13
Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands.
14
Conservation Biology, Philipps-University of Marburg, 35037, Marburg, Germany.
15
Department of Biological and Environmental Sciences, Center of Excellence in Biological Interactions, University of Jyväskylä, 40014, Jyväskylän yliopisto, Finland.
16
Institute of Plant Breeding, Seed Science and Population Genetics, 70599, Stuttgart, Germany.
17
Institute of Botany, The Czech Academy of Sciences, 25243, Průhonice, Czech Republic.
18
Institut de Recherche pour le Développement, Laboratoire Génome et Développement des Plantes, 66860, Perpignan, France.
19
Department of Ecology, Philipps-University Marburg, 35037, Marburg, Germany.
20
Plant Ecological Genomics, University of Vienna, 1030, Vienna, Austria.
21
The Santa Fe Institute, Santa Fe NM, 87501, USA.
22
Plant Cell Biology, Philipps-University Marburg, 35037, Marburg, Germany.
23
BIOSS Centre for Biological Signaling Studies, University of Freiburg, 79098, Freiburg, Germany.
24
Max Planck Institute for Mathematics in the Sciences, 04103, Leipzig, Germany.

Abstract

Growing evidence shows that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology. In plant ecology, recent studies have attempted to merge ecological experiments with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress responses, adaptation to habitat, and range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which contribute to a more mechanistic understanding but have limited ecological realism. Understanding the significance of epigenetics for plant ecology requires increased transfer of knowledge and methods from model species research to genomes of evolutionarily divergent species, and examination of responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration among molecular geneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

KEYWORDS:

Bioinformatics; ecological epigenetics; genomics; phenotypic plasticity; response to environment

PMID:
29027325
DOI:
10.1111/ele.12858
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Publication type

Publication type

8.
J Exp Bot. 2018 Jan 4;69(2):329-339. doi: 10.1093/jxb/erx254.

Diversity of cis-regulatory elements associated with auxin response in Arabidopsis thaliana.

Author information

1
Novosibirsk State University, Russian Federation.
2
Institute of Cytology and Genetics, Russian Federation.
3
Department of Agrotechnology and Food Sciences, Subdivision Biochemistry, Wageningen University and Research Center, The Netherlands.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Germany.
5
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany.

Abstract

The phytohormone auxin regulates virtually every developmental process in land plants. This regulation is mediated via de-repression of DNA-binding auxin response factors (ARFs). ARFs bind TGTC-containing auxin response cis-elements (AuxREs), but there is growing evidence that additional cis-elements occur in auxin-responsive regulatory regions. The repertoire of auxin-related cis-elements and their involvement in different modes of auxin response are not yet known. Here we analyze the enrichment of nucleotide hexamers in upstream regions of auxin-responsive genes associated with auxin up- or down-regulation, with early or late response, ARF-binding domains, and with different chromatin states. Intriguingly, hexamers potentially bound by basic helix-loop-helix (bHLH) and basic leucine zipper (bZIP) factors as well as a family of A/T-rich hexamers are more highly enriched in auxin-responsive regions than canonical TGTC-containing AuxREs. We classify and annotate the whole spectrum of enriched hexamers and discuss their patterns of enrichment related to different modes of auxin response.

KEYWORDS:

ARF; AuxRE; Auxin; bHLH; bZIP; bioinformatics; chromatin states; transcriptional regulation

9.
IEEE/ACM Trans Comput Biol Bioinform. 2018 Mar-Apr;15(2):364-376. doi: 10.1109/TCBB.2017.2696525. Epub 2017 Apr 24.

Optimal Block-Based Trimming for Next Generation Sequencing.

Abstract

Read trimming is a fundamental first step of the analysis of next generation sequencing (NGS) data. Traditionally, it is performed heuristically, and algorithmic work in this area has been neglected. Here, we address this topic and formulate three optimization problems for block-based trimming (truncating the same low-quality positions at both ends for all reads and removing low-quality truncated reads). We find that all problems are NP-hard. Hence, we investigate the approximability of the problems. Two of them are NP-hard to approximate. However, the non-random distribution of quality scores in NGS data sets makes it tempting to speculate that quality constraints for read positions are typically satisfied by fulfilling quality constraints for reads. Thus, we propose three relaxed problems and develop efficient polynomial-time algorithms for them including heuristic speed-up techniques and parallelizations. We apply these optimized block trimming algorithms to 12 data sets from three species, four sequencers, and read lengths ranging from 36 to 101 bp and find that (i) the omitted constraints are indeed almost always satisfied, (ii) the optimized read trimming algorithms typically yield a higher number of untrimmed bases than traditional heuristics, and (iii) these results can be generalized to alternative objective functions beyond counting the number of untrimmed bases.

10.
Curr Opin Genet Dev. 2017 Aug;45:69-75. doi: 10.1016/j.gde.2017.03.003. Epub 2017 Mar 24.

Cross-kingdom comparison of the developmental hourglass.

Author information

1
The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom.
2
Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Betty-Heimann-Str. 5, 06120 Halle (Saale), Germany.
3
Martin Luther University Halle-Wittenberg, Institute of Computer Science, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany; German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany.
4
Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Betty-Heimann-Str. 5, 06120 Halle (Saale), Germany. Electronic address: marcel.quint@landw.uni-halle.de.

Abstract

The developmental hourglass model has its foundations in classic anatomical studies by von Baer and Haeckel. In this context, even the conservation of animal body plans has been explained by evolutionary constraints acting on mid-embryogenic development. Recent studies have shown that developmental hourglass patterns also exist on the transcriptomic level, mirroring the corresponding morphological patterns. The identification of similar patterns in embryonic, post-embryonic, and life cycle spanning transcriptomes in plant and fungus development, however, contradict the notion of a direct coupling between morphological and molecular patterns. To explain the existence of hourglass patterns across kingdoms and developmental processes, we propose the organizational checkpoint model that integrates the developmental hourglass model into a framework of transcriptome switches.

PMID:
28347942
DOI:
10.1016/j.gde.2017.03.003
[Indexed for MEDLINE]
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11.
BMC Genomics. 2017 Mar 23;18(1):256. doi: 10.1186/s12864-017-3624-7.

Erratum to: Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens.

Author information

1
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. vincent.bs.doublet@gmail.com.
2
Centre for Ecology and Conservation, University of Exeter, Penryn, UK. vincent.bs.doublet@gmail.com.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
5
Technische Hochschule Mittelhessen, Gießen, Germany.
6
INRA, UR 406 Abeilles et Environnement, Avignon, France.
7
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali, Università degli Studi di Udine, Udine, Italy.
8
Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania.
9
Department of Biology, East Carolina University, Greenville, NC, USA.
10
Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, State College, PA, USA.
11
Present address: MRC IGMM, University of Edinburgh, Western General Hospital, Edinburgh, UK.
12
Present address: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford, UK.
13
School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK.
14
Department of Biosciences, Swansea University, Swansea, UK.
15
Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, USA.
16
Department of Entomology, Center for Pollinator Research, Pennsylvania State University, State College, PA, USA.
17
Department of Molecular Microbiology and Bee Diseases, Institute for Bee Research, Hohen Neuendorf, Germany.
18
Department of Microbiology and Epizootics, Freie Universität Berlin, Berlin, Germany.
19
Department of Fisheries, Wildlife, and Conservation Biology, The Monarch Joint Venture, University of Minnesota, St. Paul, MN, USA.
20
Department of Molecular Biology, Umeå University, Umeå, Sweden.
21
Institute for Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
22
Present address: International Centre of Insect Physiology and Ecology (icipe), Environmental Health Theme, Nairobi, Kenya.
23
School of Biological Sciences, Queen's University Belfast, Belfast, UK.
24
Institute of Biology, Freie Universität Berlin, Berlin, Germany.
25
Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
26
Department of Entomology and Nematology, University of California, Davis, CA, USA.
27
Department of Computer Science, TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany.
28
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Leipzig, Germany.
29
Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
12.
BMC Genomics. 2017 Mar 2;18(1):207. doi: 10.1186/s12864-017-3597-6.

Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens.

Author information

1
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. vincent.bs.doublet@gmail.com.
2
Centre for Ecology and Conservation, University of Exeter, Penryn, UK. vincent.bs.doublet@gmail.com.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
5
Technische Hochschule Mittelhessen, Gießen, Germany.
6
INRA, UR 406 Abeilles et Environnement, Avignon, France.
7
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali, Università degli Studi di Udine, Udine, Italy.
8
Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, Romania.
9
Department of Biology, East Carolina University, Greenville, NC, USA.
10
Center for Comparative Genomics and Bioinformatics, Pennsylvania State University, State College, PA, USA.
11
Present address: MRC IGMM, University of Edinburgh, Western General Hospital, Edinburgh, UK.
12
Present address: MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford, UK.
13
School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK.
14
Department of Biosciences, Swansea University, Swansea, UK.
15
Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT, USA.
16
Department of Entomology, Center for Pollinator Research, Pennsylvania State University, State College, PA, USA.
17
Department of Molecular Microbiology and Bee Diseases, Institute for Bee Research, Hohen Neuendorf, Germany.
18
Department of Microbiology and Epizootics, Freie Universität Berlin, Berlin, Germany.
19
Department of Fisheries, Wildlife, and Conservation Biology, The Monarch Joint Venture, University of Minnesota, St. Paul, MN, USA.
20
Department of Molecular Biology, Umeå University, Umeå, Sweden.
21
Institute for Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
22
Present address: International Centre of Insect Physiology and Ecology (icipe), Environmental Health Theme, Nairobi, Kenya.
23
School of Biological Sciences, Queen's University Belfast, Belfast, UK.
24
Institute of Biology, Freie Universität Berlin, Berlin, Germany.
25
Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
26
Department of Entomology and Nematology, University of California, Davis, CA, USA.
27
Department of Computer Science, TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Germany.
28
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Leipzig, Germany.
29
Department of Medical Microbiology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Abstract

BACKGROUND:

Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses.

RESULTS:

We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse pathogens. We found that genes differentially expressed after infection exhibit a higher evolutionary rate than non-differentially expressed genes. Using our new bioinformatics approach, we unveiled additional pathogen-specific responses of honey bees; we found that apoptosis appeared to be an important response following microsporidian infection, while genes from the immune signalling pathways, Toll and Imd, were differentially expressed after Varroa/virus infection. Finally, we applied our bioinformatics approach and generated a gene co-expression network to identify highly connected (hub) genes that may represent important mediators and regulators of anti-pathogen responses.

CONCLUSIONS:

Our meta-analysis generated a comprehensive overview of the host metabolic and other biological processes that mediate interactions between insects and their pathogens. We identified key host genes and pathways that respond to phylogenetically diverse pathogens, representing an important source for future functional studies as well as offering new routes to identify or generate pathogen resilient honey bee stocks. The statistical and bioinformatics approaches that were developed for this study are broadly applicable to synthesize information across transcriptomic datasets. These approaches will likely have utility in addressing a variety of biological questions.

KEYWORDS:

Apis mellifera; Co-expression network; DWV; IAPV; Immunity; Meta-analysis; Nosema; RNA virus; Transcriptomics; Varroa destructor

PMID:
28249569
PMCID:
PMC5333379
DOI:
10.1186/s12864-017-3597-6
[Indexed for MEDLINE]
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13.
BMC Bioinformatics. 2017 Mar 1;18(1):141. doi: 10.1186/s12859-017-1495-1.

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.

Author information

1
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. martin.nettling@informatik.uni-halle.de.
2
Leibniz Institute of Plant Biochemistry, Halle, Germany.
3
Institut d'Investigació en Intel ·ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain.
4
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
5
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.

Abstract

BACKGROUND:

Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.

RESULTS:

Here, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.

CONCLUSION:

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

KEYWORDS:

ChIP-Seq; Evolution; Gene regulation; Phylogenetic footprinting; Transcription factor binding sites

PMID:
28249564
PMCID:
PMC5333389
DOI:
10.1186/s12859-017-1495-1
[Indexed for MEDLINE]
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14.
Bioinformatics. 2017 Jun 1;33(11):1639-1646. doi: 10.1093/bioinformatics/btx033.

Unrealistic phylogenetic trees may improve phylogenetic footprinting.

Author information

1
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
2
Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany.
3
Institut d'Investigació en Intel ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain.
4
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.

Abstract

Motivation:

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.

Results:

Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.

Availability and Implementation:

The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo.

Contact:

: martin.nettling@informatik.uni-halle.de.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28130227
PMCID:
PMC5447242
DOI:
10.1093/bioinformatics/btx033
[Indexed for MEDLINE]
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15.
Front Plant Sci. 2017 Jan 9;7:2044. doi: 10.3389/fpls.2016.02044. eCollection 2016.

The Interplay of Chromatin Landscape and DNA-Binding Context Suggests Distinct Modes of EIN3 Regulation in Arabidopsis thaliana.

Author information

1
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (SB RAS), NovosibirskRussia; Department of Natural Sciences, Novosibirsk State UniversityNovosibirsk, Russia.
2
Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (SB RAS), Novosibirsk Russia.
3
Department of Natural Sciences, Novosibirsk State UniversityNovosibirsk, Russia; Institute of Computer Science, Martin Luther University Halle-WittenbergHalle(Saale), Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigLeipzig, Germany.

Abstract

The plant hormone ethylene regulates numerous developmental processes and stress responses. Ethylene signaling proceeds via a linear pathway, which activates transcription factor (TF) EIN3, a primary transcriptional regulator of ethylene response. EIN3 influences gene expression upon binding to a specific sequence in gene promoters. This interaction, however, might be considerably affected by additional co-factors. In this work, we perform whole genome bioinformatics study to identify the impact of epigenetic factors in EIN3 functioning. The analysis of publicly available ChIP-Seq data on EIN3 binding in Arabidopsis thaliana showed bimodality of distribution of EIN3 binding regions (EBRs) in gene promoters. Besides a sharp peak in close proximity to transcription start site, which is a common binding region for a wide variety of TFs, we found an additional extended peak in the distal promoter region. We characterized all EBRs with respect to the epigenetic status appealing to previously published genome-wide map of nine chromatin states in A. thaliana. We found that the implicit distal peak was associated with a specific chromatin state (referred to as chromatin state 4 in the primary source), which was just poorly represented in the pronounced proximal peak. Intriguingly, EBRs corresponding to this chromatin state 4 were significantly associated with ethylene response, unlike the others representing the overwhelming majority of EBRs related to the explicit proximal peak. Moreover, we found that specific EIN3 binding sequences predicted with previously described model were enriched in the EBRs mapped to the chromatin state 4, but not to the rest ones. These results allow us to conclude that the interplay of genetic and epigenetic factors might cause the distinct modes of EIN3 regulation.

KEYWORDS:

ChIP-Seq; EIN3 binding site (EBS); ETHYLENE-INSENSITIVE3; Gene Ontology; TEIL; bioinformatics; position weight matrix; transcriptional regulation

17.
Bioinformatics. 2017 Feb 15;33(4):580-582. doi: 10.1093/bioinformatics/btw689.

InMoDe: tools for learning and visualizing intra-motif dependencies of DNA binding sites.

Author information

1
Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
3
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.

Abstract

Summary:

Recent studies have shown that the traditional position weight matrix model is often insufficient for modeling transcription factor binding sites, as intra-motif dependencies play a significant role for an accurate description of binding motifs. Here, we present the Java application InMoDe, a collection of tools for learning, leveraging and visualizing such dependencies of putative higher order. The distinguishing feature of InMoDe is a robust model selection from a class of parsimonious models, taking into account dependencies only if justified by the data while choosing for simplicity otherwise.

Availability and Implementation:

InMoDe is implemented in Java and is available as command line application, as application with a graphical user-interface, and as an integration into Galaxy on the project website at http://www.jstacs.de/index.php/InMoDe .

Contact:

ralf.eggeling@cs.helsinki.fi.

PMID:
28035026
PMCID:
PMC5408807
DOI:
10.1093/bioinformatics/btw689
[Indexed for MEDLINE]
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18.
Front Plant Sci. 2016 Nov 14;7:1662. eCollection 2016.

A Detailed Analysis of the BR1 Locus Suggests a New Mechanism for Bolting after Winter in Sugar Beet (Beta vulgaris L.).

Author information

1
Plant Breeding Institute, University of Kiel Kiel, Germany.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg Halle, Germany.
3
Institute of Clinical Molecular Biology, University of Kiel Kiel, Germany.
4
Institute of Computer Science, Martin Luther University Halle-WittenbergHalle, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Leipzig-JenaLeipzig, Germany.

Abstract

Sugar beet (Beta vulgaris ssp. vulgaris) is a biennial, sucrose-storing plant, which is mainly cultivated as a spring crop and harvested in the vegetative stage before winter. For increasing beet yield, over-winter cultivation would be advantageous. However, bolting is induced after winter and drastically reduces yield. Thus, post-winter bolting control is essential for winter beet cultivation. To identify genetic factors controlling bolting after winter, a F2 population was previously developed by crossing the sugar beet accessions BETA 1773 with reduced bolting tendency and 93161P with complete bolting after winter. For a mapping-by-sequencing analysis, pools of 26 bolting-resistant and 297 bolting F2 plants were used. Thereby, a single continuous homozygous region of 103 kb was co-localized to the previously published BR1 QTL for post-winter bolting resistance (Pfeiffer et al., 2014). The BR1 locus was narrowed down to 11 candidate genes from which a homolog of the Arabidopsis CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR 73-I (CPSF73-I) was identified as the most promising candidate. A 2 bp deletion within the BETA 1773 allele of BvCPSF73-Ia results in a truncated protein. However, the null allele of BvCPSF73-Ia might partially be compensated by a second BvCPSF73-Ib gene. This gene is located 954 bp upstream of BvCPSF73-Ia and could be responsible for the incomplete penetrance of the post-winter bolting resistance allele of BETA 1773. This result is an important milestone for breeding winter beets with complete bolting resistance after winter.

KEYWORDS:

flowering time; mapping-by-sequencing; sugar beet; vernalization; winter beet

19.
BMC Genomics. 2016 Nov 24;17(1):969.

Phylogenetic distribution of plant snoRNA families.

Author information

1
Bioinformatics Group, Dept. Computer Science, and artin-Luther-Universität Halle-Wittenberg, Leipzig, D-04107, Germany.
2
Institut für Informatik, Halle (Saale), D-06120, Germany.
3
Young Investigators Group Bioinformatics & Transcriptomics, Helmholtz Centre for Environmental Research - UFZ, Permoserstrasse 15, Leipzig, D-04318, Germany.
4
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany.
5
Bioinformatics Group, Dept. Computer Science, and artin-Luther-Universität Halle-Wittenberg, Leipzig, D-04107, Germany. studla@bioinf.uni-leipzig.de.
6
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103, Germany. studla@bioinf.uni-leipzig.de.
7
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, Leipzig, D-04103, Germany. studla@bioinf.uni-leipzig.de.
8
Department of Theoretical Chemistry of the University of Vienna, Währingerstrasse 17, Leipzig, A-1090, Germany. studla@bioinf.uni-leipzig.de.
9
Center for RNA in Technology and Health, Univ. Copenhagen, Grønnegårdsvej 3, Frederiksberg C, Copenhagen, Denmark. studla@bioinf.uni-leipzig.de.
10
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.
11
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany. studla@bioinf.uni-leipzig.de.

Abstract

BACKGROUND:

Small nucleolar RNAs (snoRNAs) are one of the most ancient families amongst non-protein-coding RNAs. They are ubiquitous in Archaea and Eukarya but absent in bacteria. Their main function is to target chemical modifications of ribosomal RNAs. They fall into two classes, box C/D snoRNAs and box H/ACA snoRNAs, which are clearly distinguished by conserved sequence motifs and the type of chemical modification that they govern. Similarly to microRNAs, snoRNAs appear in distinct families of homologs that affect homologous targets. In animals, snoRNAs and their evolution have been studied in much detail. In plants, however, their evolution has attracted comparably little attention.

RESULTS:

In order to chart the phylogenetic distribution of individual snoRNA families in plants, we applied a sophisticated approach for identifying homologs of known plant snoRNAs across the plant kingdom. In response to the relatively fast evolution of snoRNAs, information on conserved sequence boxes, target sequences, and secondary structure is combined to identify additional snoRNAs. We identified 296 families of snoRNAs in 24 species and traced their evolution throughout the plant kingdom. Many of the plant snoRNA families comprise paralogs. We also found that targets are well-conserved for most snoRNA families.

CONCLUSIONS:

The sequence conservation of snoRNAs is sufficient to establish homologies between phyla. The degree of this conservation tapers off, however, between land plants and algae. Plant snoRNAs are frequently organized in highly conserved spatial clusters. As a resource for further investigations we provide carefully curated and annotated alignments for each snoRNA family under investigation.

KEYWORDS:

Evolution; Small RNAs; snoRNA targets; snoRNAs

PMID:
27881081
PMCID:
PMC5122169
DOI:
10.1186/s12864-016-3301-2
[Indexed for MEDLINE]
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20.
Sci Rep. 2016 Oct 7;6:34589. doi: 10.1038/srep34589.

Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells.

Author information

1
RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
2
Institute of Virology, Philipps University Marburg, Hans-Meerwein-Str. 2, 35043 Marburg, Germany.
3
German Center for Infection Research (DZIF), partner site Gießen-Marburg-Langen, Hans-Meerwein Str. 2, 35043, Marburg, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
5
FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745 Jena, Germany.
6
Transcriptome Bioinformatics, Junior Research Group, Leipzig Research Center for Civilization Diseases, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
7
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
8
Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
9
Theoretical Biochemistry Group, Institute of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria.
10
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg, Germany.
11
Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
12
Institute of Computer Science, Martin-Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle/Saale, Germany.
13
Department of Soil Ecology, UFZ - Helmholtz Centre for Environmental Research, Theodor-Lieser-Str. 4, 06120, Halle/Saale, Germany.
14
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
15
Biozentrum, University of Basel, Klingelbergstraße 50/70, CH-4056, Basel, Switzerland.
16
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
17
Junior Professorship for Computational EvoDevo, Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
18
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
19
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 54, 04109, Leipzig, Germany.
20
Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI), Systems Biology and Bioinformatics, Beutenbergstraße 11a, 07745, Jena, Germany.
21
Department of Bioanalytical Ecotoxicology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.
22
Doctoral School of Science and Technology, AZM Center for Biotechnology Research, Lebanese University, Tripoli, Lebanon.
23
TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
24
Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, Scotland, U.K.
25
Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090, Vienna, Austria.
26
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
27
Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, 1090, Vienna, Austria.
28
Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Brunswiker Str. 10, 24105, Kiel, Germany.

Abstract

The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.

PMID:
27713552
PMCID:
PMC5054393
DOI:
10.1038/srep34589
[Indexed for MEDLINE]
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