30 research outputs found
Considerations and complications of mapping small RNA high-throughput data to transposable elements
BACKGROUND
High-throughput sequencing (HTS) has revolutionized the way in which epigenetic research is conducted. When coupled with fully-sequenced genomes, millions of small RNA (sRNA) reads are mapped to regions of interest and the results scrutinized for clues about epigenetic mechanisms. However, this approach requires careful consideration in regards to experimental design, especially when one investigates repetitive parts of genomes such as transposable elements (TEs), or when such genomes are large, as is often the case in plants.
RESULTS
Here, in an attempt to shed light on complications of mapping sRNAs to TEs, we focus on the 2,300 Mb maize genome, 85% of which is derived from TEs, and scrutinize methodological strategies that are commonly employed in TE studies. These include choices for the reference dataset, the normalization of multiply mapping sRNAs, and the selection among sRNA metrics. We further examine how these choices influence the relationship between sRNAs and the critical feature of TE age, and contrast their effect on low copy genomic regions and other popular HTS data.
CONCLUSIONS
Based on our analyses, we share a series of take-home messages that may help with the design, implementation, and interpretation of high-throughput TE epigenetic studies specifically, but our conclusions may also apply to any work that involves analysis of HTS data
A role for palindromic structures in the cis-region of maize Sirevirus LTRs in transposable element evolution and host epigenetic response
Transposable elements (TEs) proliferate within the genome of their host, which responds by silencing them epigenetically. Much is known about the mechanisms of silencing in plants, particularly the role of siRNAs in guiding DNA methylation. In contrast, little is known about siRNA targeting patterns along the length of TEs, yet this information may provide crucial insights into the dynamics between hosts and TEs. By focusing on 6456 carefully annotated, full-length Sirevirus LTR retrotransposons in maize, we show that their silencing associates with underlying characteristics of the TE sequence and also uncover three features of the host–TE interaction. First, siRNA mapping varies among families and among elements, but particularly along the length of elements. Within the cis-regulatory portion of the LTRs, a complex palindrome-rich region acts as a hotspot of both siRNA matching and sequence evolution. These patterns are consistent across leaf, tassel, and immature ear libraries, but particularly emphasized for floral tissues and 21- to 22-nt siRNAs. Second, this region has the ability to form hairpins, making it a potential template for the production of miRNA-like, hairpin-derived small RNAs. Third, Sireviruses are targeted by siRNAs as a decreasing function of their age, but the oldest elements remain highly targeted, partially by siRNAs that cross-map to the youngest elements. We show that the targeting of older Sireviruses reflects their conserved palindromes. Altogether, we hypothesize that the palindromes aid the silencing of active elements and influence transposition potential, siRNA targeting levels, and ultimately the fate of an element within the genome
Modeling interactions between transposable elements and the plant epigenetic response: a surprising reliance on element retention
Transposable elements (TEs) compose the majority of angiosperm DNA. Plants counteract TE activity by silencing them epigenetically. One form of epigenetic silencing requires 21-22 nt small interfering RNAs that act to degrade TE mRNA and may also trigger DNA methylation. DNA methylation is reinforced by a second mechanism, the RNA-dependent DNA methylation (RdDM) pathway. RdDM relies on 24 nt small interfering RNAs and ultimately establishes TEs in a quiescent state. These host factors interact at a systems level, but there have been no system level analyses of their interactions. Here, we define a deterministic model that represents the propagation of active TEs, aspects of the host response and the accumulation of silenced TEs. We describe general properties of the model and also fit it to biological data in order to explore two questions. The first is why two overlapping pathways are maintained, given that both are likely energetically expensive. Under our model, RdDM silenced TEs effectively even when the initiation of silencing was weak. This relationship implies that only a small amount of RNAi is needed to initiate TE silencing, but reinforcement by RdDM is necessary to efficiently counter TE propagation. Second, we investigated the reliance of the host response on rates of TE deletion. The model predicted that low levels of deletion lead to few active TEs, suggesting that silencing is most efficient when methylated TEs are retained in the genome, thereby providing one explanation for the large size of plant genomes
Integrating transposable elements in the 3D genome
Chromosome organisation is increasingly recognised as an essential component of genome regulation, cell fate and cell health. Within the realm of transposable elements (TEs) however, the spatial information of how genomes are folded is still only rarely integrated in experimental studies or accounted for in modelling. Whilst polymer physics is recognised as an important tool to understand the mechanisms of genome folding, in this commentary we discuss its potential applicability to aspects of TE biology. Based on recent works on the relationship between genome organisation and TE integration, we argue that existing polymer models may be extended to create a predictive framework for the study of TE integration patterns. We suggest that these models may offer orthogonal and generic insights into the integration profiles (or "topography") of TEs across organisms. In addition, we provide simple polymer physics arguments and preliminary molecular dynamics simulations of TEs inserting into heterogeneously flexible polymers. By considering this simple model, we show how polymer folding and local flexibility may generically affect TE integration patterns. The preliminary discussion reported in this commentary is aimed to lay the foundations for a large-scale analysis of TE integration dynamics and topography as a function of the three-dimensional host genome
Treatment- and Population-Dependent Activity Patterns of Behavioral and Expression QTLs
Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol
The genome-wide dynamics of purging during selfing in maize
Self-fertilization (also known as selfing) is an important reproductive strategy in plants and a widely applied tool for plant genetics and plant breeding. Selfing can lead to inbreeding depression by uncovering recessive deleterious variants, unless these variants are purged by selection. Here we investigated the dynamics of purging in a set of eleven maize lines that were selfed for six generations. We show that heterozygous, putatively deleterious single nucleotide polymorphisms are preferentially lost from the genome during selfing. Deleterious single nucleotide polymorphisms were lost more rapidly in regions of high recombination, presumably because recombination increases the efficacy of selection by uncoupling linked variants. Overall, heterozygosity decreased more slowly than expected, by an estimated 35% to 40% per generation instead of the expected 50%, perhaps reflecting pervasive associative overdominance. Finally, three lines exhibited marked decreases in genome size due to the purging of transposable elements. Genome loss was more likely to occur for lineages that began with larger genomes with more transposable elements and chromosomal knobs. These three lines purged an average of 398 Mb from their genomes, an amount equivalent to three Arabidopsis thaliana genomes per lineage, in only a few generations
Ty1-copia elements reveal diverse insertion sites linked to polymorphisms among flax (Linum usitatissimum L.) accessions
Changes in the composition of the RNA virome mark evolutionary transitions in green plants
Background: The known plant viruses mostly infect angiosperm hosts and have RNA or small DNA genomes. The only other lineage of green plants with a relatively well-studied virome, unicellular chlorophyte algae, is mostly infected by viruses with large DNA genomes. Thus RNA viruses and small DNA viruses seem to completely displace large DNA virus genomes in late branching angiosperms. To understand better the expansion of RNA viruses in the taxonomic span between algae and angiosperms, we analyzed the transcriptomes of 66 non-angiosperm plants characterized by the 1000 Plants Genomes Project.
Results: We found homologs of virus RNA-dependent RNA polymerases in 28 non-angiosperm plant species, including algae, mosses, liverworts (Marchantiophyta), hornworts (Anthocerotophyta), lycophytes, a horsetail Equisetum, and gymnosperms. Polymerase genes in algae were most closely related to homologs from double-stranded RNA viruses leading latent or persistent lifestyles. Land plants, in addition, contained polymerases close to the homologs from single-stranded RNA viruses of angiosperms, capable of productive infection and systemic spread. For several polymerases, a cognate capsid protein was found in the same library. Another virus hallmark gene family, encoding the 30 K movement proteins, was found in lycophytes and monilophytes but not in mosses or algae.
Conclusions: The broadened repertoire of RNA viruses suggests that colonization of land and growth in anatomical complexity in land plants coincided with the acquisition of novel sets of viruses with different strategies of infection and reproduction.We thank the colleagues at the 1000 Plant Genomes Project for helping us to access the transcriptomes used in this study via the iPlant Collaborative. We are grateful to Javier Forment (IBMCP-CSIC), Vincent Lefort (PhyML), and the E-Biothon team (E-Biothon platform is supported by CNRS, IBM, INRIA, l'Institut Francais de Bioinformatique and SysFera) for expert help with high-performance computing; to Yuri Wolf, Jan Kreuze, Eddie Holmes, and Mang Shi for sharing sequence data and alignments; to Sejo Sabanadzovic, Jan Kreuze, and the anonymous reviewers for helpful virtual discussions and critical remarks; and to Natalia Mushegian for technical assistance. SFF was supported by grants BFU2015-65037P from Spain Ministry of Economy and Competitiveness and PROMETEOII/2014/021 from Generalitat Valenciana. ARM is a Program Director at the US National Science Foundation (NSF); his work on this project was supported by the NSF Independent Research and Development Program, but the statements and opinions expressed herein are made in the personal capacity and do not constitute the endorsement by NSF or the government of the United States.Mushegian, A.; Shipunov, A.; Elena Fito, SF. (2016). Changes in the composition of the RNA virome mark evolutionary transitions in green plants. BMC Biology. 14(68):1-14. https://doi.org/10.1186/s12915-016-0288-8S1141468Roossinck MJ. Plant virus metagenomics: biodiversity and ecology. Annu Rev Genet. 2012;46:357–67.Koonin EV, Dolja VV, Krupovic M. Origins and evolution of viruses of eukaryotes: The ultimate modularity. Virology. 2015;479-480:2–25.Yamada T, Onimatsu H, Van Etten JL. Chlorella viruses. Adv Virus Res. 2006;66:293–336.Van Etten JL, Dunigan DD. Chloroviruses: not your everyday plant virus. Trends Plant Sci. 2012;17:1–8.Zhang T, Jiang Y, Dong W. A novel monopartite dsRNA virus isolated from the phytopathogenic fungus Ustilaginoidea virens and ancestrally related to a mitochondria-associated dsRNA in the green alga Bryopsis. Virology. 2014;462:227–35.Wilson WH, Van Etten JL, Allen MJ. The Phycodnaviridae: the story of how tiny giants rule the world. Curr Top Microbiol Immunol. 2009;328:1–42.Iyer LM, Balaji S, Koonin EV, Aravind L. Evolutionary genomics of nucleo-cytoplasmic large DNA viruses. Virus Res. 2006;117:156–84.Colson P, De Lamballerie X, Yutin N, Asgari S, Bigot Y, Bideshi DK, et al. “Megavirales”, a proposed new order for eukaryotic nucleocytoplasmic large DNA viruses. Arch Virol. 2013;158:2517–21.Gibbs AJ, Torronen M, Mackenzie AM, Wood 2nd JT, Amstrong JS, Kondo H, et al. The enigmatic genome of Chara australis virus. J Gen Virol. 2011;92:2679–90.Valverde RA, Sabanadzovic S. A novel plant virus with unique properties infecting Japanese holly fern. J Gen Virol. 2009;90:42–9.Han SS, Karasev AV, Ieki H, Iwanami T. Nucleotide sequence and taxonomy of Cycas necrotic stunt virus. Brief report. Arch Virol. 2002;147:2207–14.Lockhart B, Fetzer JL, Olszewski NE. Preliminary characterization of Cycad leaf necrosis virus, the first badnavirus identified in cycads. Phytopathology. 2006;96:S70.Mushegian AR, Elena SF. Evolution of plant virus movement proteins from the 30 K superfamily and of their homologs integrated in plant genomes. Virology. 2015;476:304–15.Maumus F, Epert A, Nogué F, Blanc G. Plant genomes enclose footprints of past infections by giant virus relatives. Nat Commun. 2014;5:4268.Wickett NJ, Mirarab S, Nguyen N, Warnow T, Carpenter E, Matasci N, et al. Phylotranscriptomic analysis of the origin and early diversification of land plants. Proc Natl Acad Sci USA. 2014;111:E4859–68.Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D, et al. The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Biol. 2016;14:e1002342.Becker B, Marin B. Streptophyte algae and the origin of embryophytes. Ann Bot. 2009;103:999–1004.Koonin EV, Dolja VV. A virocentric perspective on the evolution of life. Curr Op Virol. 2013;3:546–57.Matasci N, Huang LH, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, et al. Data access for the 1000 plants (1KP) project. Gigascience. 2014;3:17.Makarova KS, Aravind L, Grishin NV, Rogozin IB, Koonin EV. A DNA repair system specific for thermophilic Archaea and bacteria predicted by genomic context analysis. Nucleic Acids Res. 2002;30:482–96.Majumdar I, Kinch LN, Grishin NV. A database of domain definitions for proteins with complex interdomain geometry. PLoS ONE. 2009;4:e5084.Černý J, Černá Bolfíková B, Zanotto PMA, Grubhoffer L, Růžek D. A deep phylogeny of viral and cellular right-hand polymerases. Infec Genet Evol. 2015;36:275–86.Iyer LM, Koonin EV, Aravind L. Evolutionary connection between the catalytic subunits of DNA-dependent RNA polymerases and eukaryotic RNA-dependent RNA polymerases and the origin of RNA polymerases. BMC Struct Biol. 2003;3:1.Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.Koga R, Horiuchi H, Fukuhara T. Double-stranded RNA replicons associated with chloroplasts of a green alga, Bryopsis cinicola. Plant Mol Biol. 2003;51:991–9.Jablonski SA, Morrow CD. Mutation of the aspartic acid residues of the GDD sequence motif of poliovirus RNA-dependent RNA polymerase results in enzymes with altered metal ion requirements for activity. J Virol. 1995;69:1532–9.Morin B, Whelan SPJ. La protéine L des Mononegavirales. Virologie. 2012;16:258–68.Liang B, Li Z, Jenni S, Rahmeh AA, Morin BM, Grant T, et al. Structure of the L protein of Vesicular stomatitis virus from electron cryomicroscopy. Cell. 2015;162:314–27.Chabannes M, Iskra-Caruana ML. Endogenous pararetroviruses – a reservoir of virus infection in plants. Curr Opin Virol. 2013;3:615–20.Bousios A, Darzentas N. Sirevirus LTR retrotransposons: phylogenetic misconceptions in the plant world. Mob DNA. 2013;4:9.Bertsch C, Beuve M, Dolja VV, Wirth M, Pelsy F, Herrbach E, et al. Retention of the virus-derived sequences in the nuclear genome of grapevine as a potential pathway to virus resistance. Biol Direct. 2009;4:21.Roossinck MJ. Lifestyle of plant viruses. Philos Trans R Soc B. 2010;365:1899–905.Nibert ML, Ghabrial SA, Maiss E, Lesker T, Vainio EJ, Jiang D, et al. Taxonomic reorganization of family Partitiviridae and other recent progress in partitivirus research. Virus Res. 2014;188:128–41.Koonin EV, Wolf YI, Nagasaki K, Dolja VV. The Big Bang of picorna-like virus evolution antedates the radiation of eukaryotic supergroups. Nat Rev Microbiol. 2008;6:925–39.Poch O, Sauvaget I, Delarue M, Tordo N. Identification of four conserved motifs among the RNA-dependent polymerase encoding elements. EMBO J. 1989;8:3867–74.Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44(W1):W242.Li CX, Shi M, Tian JH, Lin XD, Kang YJ, Chen LJ, et al. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife. 2015;29:4.Yutin N, Wolf YI, Raoult D, Koonin EV. Eukaryotic large nucleo-cytoplasmic DNA viruses: clusters of orthologous genes and reconstruction of viral genome evolution. Virol J. 2009;6:223.Liu H, Fu Y, Xie J, Cheng J, Ghabrial SA, Li G, et al. Evolutionary genomics of mycovirus-related dsRNA viruses reveals cross-family horizontal gene transfer and evolution of diverse viral lineages. BMC Evol Biol. 2012;12:91.Atsatt PR, Whiteside MD. Novel symbiotic protoplasts formed by endophytic fungi explain their hidden existence, lifestyle switching, and diversity within the plant kingdom. PLoS ONE. 2014;9:e95266.Hom EF, Murray AW. Plant-fungal ecology. Niche engineering demonstrates a latent capacity for fungal-algal mutualism. Science. 2014;345:94–8.Magallón S, Hilu KW, Quandt D. Land plant evolutionary timeline: gene effects are secondary to fossil constraints in relaxed clock estimation of age and substitution rates. Am J Bot. 2013;100:556–73.Johnson MT, Carpenter EJ, Tian Z, Bruskiewich R, Burris JN, Carrigan CT, et al. Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes. PLoS One. 2012;7:e50226.Kreuze J. siRNA deep sequencing and assembly: piecing together viral infections. In: Gullino ML, Bonants PJM, editors. Detection and diagnostics of plant pathogens. Dordrecht: Springer; 2014. p. 21–38.Chávez Montes RA, de Fátima R-CF, De Paoli E, Accerbi M, Rymarquis LA, Mahalingam G, et al. Sample sequencing of vascular plants demonstrates widespread conservation and divergence of microRNAs. Nat Commun. 2014;5:3722.Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, et al. CDD: NCBI’s conserved domain database. Nucl Acids Res. 2015;43:D222–6.Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113.Söding J, Biegert A, Lupas AN. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2015;33:W244–8.Le SQ, Gascuel O. An improved general amino acid replacement matrix. Mol Biol Evol. 2008;25(7):1307–20.Daydé M, Depardon B, Franc A, Gibrat JF, Guillier R, Karami Y, Sutter F, Taddese B, Chabbert M, Thérond S. E-Biothon: an experimental platform for bioinformatics. 2015 Computer Science and Information Technologies (CSIT) 2015;1-4.Okuno K, Hama T, Takeshita M, Furuya N, Takanami Y. New potyvirus isolated from Cryptotaenia japonica. J Gen Plant Pathol. 2003;69:138–42
Effects of gender and stress on the regulation of steroid receptor coactivator-1 expression in the rat brain and pituitary
Steroid/thyroid actions in the brain are exerted through their receptors
which belong to the nuclear receptor superfamily. Transcriptional
transactivation mediated by these receptors depends on recruited
co-activators, among which steroid receptor co-activators (SRCs) seem to
be restricted to the nuclear receptor family. By using Northern and
Western blot analysis we have estimated the mRNA and protein levels,
respectively, of SRC-1 in the brain and pituitary of male and female
rats, under physiological conditions and Following restraint stress.
Under basal conditions, SRC-1 is expressed at higher levels in the
hippocampus and the pituitary of male, compared to female rats. Acute
stress results in decreased, compared to the control, SRC-1 levels in
the hypothalamus of both sexes, in the pituitary and frontal cortex of
male rats, and in increased SRC-1 levels in the hippocampus of female
rats. The observed changes at the mRNA level are supported by analogous
changes at the protein level. The apparent regulation of SRC-1 gene
expression in the nervous system by the endocrine status of the animal,
adds another level of complexity in the mechanism controlling steroid
hormone actions, Furthermore, the variability in SRC-l expression within
the brain provides a means to explain the cell-specificity of steroid
hormone actions in this tissue. (C) 2001 Elsevier Science Ltd. All
rights reserved