Effects of macroalgae loss in an Antarctic marine food web: applying extinction thresholds to food web studies

Antarctica is seriously affected by climate change, particularly at the Western Antarctic Peninsula (WAP) where a rapid regional warming is observed. Potter Cove is a WAP fjord at Shetland Islands that constitutes a biodiversity hotspot where over the last years, Potter Cove annual air temperatures averages increased by 0.66 °C, coastal glaciers declined, and suspended particulate matter increased due to ice melting. Macroalgae are the main energy source for all consumers and detritivores of Potter Cove. Some effects of climate change favor pioneer macroalgae species that exploit new ice-free areas and can also decline rates of photosynthesis and intensify competition between species due to the increase of suspended particulate matter. In this study, we evaluated possible consequences of climate change at Potter Cove food web by simulating the extinction of macroalgae and detritus using a topological approach with thresholds of extinction. Thresholds represent the minimum number of incoming links necessary for species’ survival. When we simulated the extinctions of macroalgae species at random, a threshold of extinction beyond 50% was necessary to obtain a significant number of secondary extinctions, while with a 75% threshold a real collapse of the food web occurred. Our results indicate that Potter Cove food web is relative robust to macroalgae extinction. This is dramatically different from what has been found in other food webs, where the reduction of 10% in prey intake caused a disproportionate increase of secondary extinctions. Robustness of the Potter Cove food web was mediated by omnivory and redundancy, which had an important relevance in this food web. When we eliminated larger-biomass species more secondary extinctions occurred, a similar response was observed when more connected species were deleted, yet there was no correlation between species of larger-biomass and high-degree. This similarity could be explained because both criteria involved key species that produced an emerging effect on the food web. In this way, large-biomass and high-degree species could be acting as source for species with few trophic interactions or low redundancy. Based on this work, we expect the Potter Cove food web to be robust to changes in macroalgae species caused by climate change until a high threshold of stress is reached, and then negative effects are expected to spread through the entire food web leading to its collapse

Chromospheric activity and rotation of FGK stars in the solar vicinity. An estimation of the radial velocity jitter

Context: Chromospheric activity produces both photometric and spectroscopic variations that can be mistaken as planets. Large spots crossing the stellar disc can produce planet-like periodic variations in the light curve of a star. These spots clearly affect the spectral line profiles and their perturbations alter the line centroids creating a radial velocity jitter that might contaminate" the variations induced by a planet. Precise chromospheric activity measurements are needed to estimate the activity-induced noise that should be expected for a given star. Aims: We obtain precise chromospheric activity measurements and projected rotational velocities for nearby (d < 25 pc) cool (spectral types F to K) stars, to estimate their expected activity-related jitter. As a complementary objective, we attempt to obtain relationships between fluxes in different activity indicator lines, that permit a transformation of traditional activity indicators, i.e, CaII H & K lines, to others that hold noteworthy advantages. Methods: We used high resolution (~50000) echelle optical spectra. To determine the chromospheric emission of the stars in the sample, we used the spectral subtraction technique. Rotational velocities were determined using the cross-correlation technique. To infer activity-related radial velocity (RV) jitter, we used empirical relationships between this jitter and the R'_HK index. Results: We measured chromospheric activity, as given by different indicators throughout the optical spectra, and projected rotational velocities for 371 nearby cool stars. We have built empirical relationships among the most important chromospheric emission lines. Finally, we used the measured chromospheric activity to estimate the expected RV jitter for the active stars in the sample.Comment: Accepted for publication in Astronomy & Astrophysic

Outlying HII Regions in HI-Selected Galaxies

We present results from the first systematic search for outlying HII regions, as part of a sample of 96 emission-line point sources (referred to as ELdots - emission-line dots) derived from the NOAO Survey for Ionization in Neutral Gas Galaxies (SINGG). Our automated ELdot-finder searches SINGG narrow-band and continuum images for high equivalent width point sources outside the optical radius of the target galaxy (> 2 X r25 in the R-band). Follow-up longslit spectroscopy and deep GALEX images (exposure time > 1000 s) distinguish outlying HII regions from background galaxies whose strong emission lines ([OIII], Hbeta or [OII]) have been redshifted into the SINGG bandpass. We find that these deep GALEX images can serve as a substitute for spectroscopic follow-up because outlying HII regions separate cleanly from background galaxies in color-color space. We identify seven SINGG systems with outlying massive star formation that span a large range in Halpha luminosities corresponding to a few O stars in the most nearby cases, and unresolved dwarf satellite companion galaxies in the most distant cases. Six of these seven systems feature galaxies with nearby companions or interacting galaxies. Furthermore, our results indicate that some outlying HII regions are linked to the extended-UV disks discovered by GALEX, representing emission from the most massive O stars among a more abundant population of lower mass (or older) star clusters. The overall frequency of outlying HII regions in this sample of gas-rich galaxies is 8 - 11% when we correct for background emission-line galaxy contamination (~75% of ELdots).Comment: 20 pages, 14 Figures, Accepted by A

Search for Cold Debris Disks around M-dwarfs

Debris disks are believed to be related to planetesimals left over around stars after planet formation has ceased. The frequency of debris disks around M-dwarfs which account for 70% of the stars in the Galaxy is unknown while constrains have already been found for A- to K-type stars. We have searched for cold debris disks around 32 field M-dwarfs by conducting observations at lambda = 850 microns with the SCUBA bolometerarray camera at the JCMT and at lambda = 1.2mm with the MAMBO array at the IRAM 30-m telescopes. This is the first survey of a large sample of M-dwarfs conducted to provide statistical constraints on debris disks around this type of stars. We have detected a new debris disk around the M0.5 dwarf GJ842.2 at lambda = 850 microns, providing evidence for cold dust at large distance from this star (~ 300AU). By combining the results of our survey with the ones of Liu et al. (2004), we estimate for the first time the detection rate of cold debris disks around field M-dwarfs with ages between 20 and 200 Myr. This detection rate is 13^{+6}_{-8} % and is consistent with the detection rate of cold debris disks (9 - 23 %) around A- to K-type main sequence stars of the same age. This is an indication that cold disks may be equally prevalent across stellar spectral types.Comment: A&A accepted on 15 september 200

Location of chlorogenic acid biosynthesis pathway and polyphenol oxidase genes in a new interspecific anchored linkage map of eggplant

© Gramazio et al.; licensee BioMed Central. 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Photometric transit search for planets around cool stars from the western Italian Alps: A pilot study

[ABRIDGED] In this study, we set out to a) demonstrate the sensitivity to <4 R_E transiting planets with periods of a few days around our program stars, and b) improve our knowledge of some astrophysical properties(e.g., activity, rotation) of our targets by combining spectroscopic information and our differential photometric measurements. We achieve a typical nightly RMS photometric precision of ~5 mmag, with little or no dependence on the instrumentation used or on the details of the adopted methods for differential photometry. The presence of correlated (red) noise in our data degrades the precision by a factor ~1.3 with respect to a pure white noise regime. Based on a detailed stellar variability analysis, a) we detected no transit-like events; b) we determined photometric rotation periods of ~0.47 days and ~0.22 days for LHS 3445 and GJ 1167A, respectively; c) these values agree with the large projected rotational velocities (~25 km/s and ~33 km/s, respectively) inferred for both stars based on the analysis of archival spectra; d) the estimated inclinations of the stellar rotation axes for LHS 3445 and GJ 1167A are consistent with those derived using a simple spot model; e) short-term, low-amplitude flaring events were recorded for LHS 3445 and LHS 2686. Finally, based on simulations of transit signals of given period and amplitude injected in the actual (nightly reduced) photometric data for our sample, we derive a relationship between transit detection probability and phase coverage. We find that, using the BLS search algorithm, even when phase coverage approaches 100%, there is a limit to the detection probability of ~90%. Around program stars with phase coverage >50% we would have had >80% chances of detecting planets with P0.5%, corresponding to minimum detectable radii in the range 1.0-2.2 R_E. [ABRIDGED]Comment: 23 pages, 17 figures, 7 tables. Accepted for publication in MNRA

SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species

[EN] Background The use of sequencing and genotyping platforms has undergone dramatic improvements, enabling the generation of a wealth of genomic information. Despite this progress, the availability of high-quality genomic DNA (gDNA) in sufficient concentrations is often a main limitation, especially for third-generation sequencing platforms. A variety of DNA extraction methods and commercial kits are available. However, many of these are costly and frequently give either low yield or low-quality DNA, inappropriate for next generation sequencing (NGS) platforms. Here, we describe a fast and inexpensive DNA extraction method (SILEX) applicable to a wide range of plant species and tissues. Results SILEX is a high-throughput DNA extraction protocol, based on the standard CTAB method with a DNA silica matrix recovery, which allows obtaining NGS-quality high molecular weight genomic plant DNA free of inhibitory compounds. SILEX was compared with a standard CTAB extraction protocol and a common commercial extraction kit in a variety of species, including recalcitrant ones, from different families. In comparison with the other methods, SILEX yielded DNA in higher concentrations and of higher quality. Manual extraction of 48 samples can be done in 96 min by one person at a cost of 0.12 euro/sample of reagents and consumables. Hundreds of tomato gDNA samples obtained with either SILEX or the commercial kit were successfully genotyped with Single Primer Enrichment Technology (SPET) with the Illumina HiSeq 2500 platform. Furthermore, DNA extracted fromSolanum elaeagnifoliumusing this protocol was assessed by Pulsed-field gel electrophoresis (PFGE), obtaining a suitable size ranges for most sequencing platforms that required high-molecular-weight DNA such as Nanopore or PacBio. Conclusions A high-throughput, fast and inexpensive DNA extraction protocol was developed and validated for a wide variety of plants and tissues. SILEX offers an easy, scalable, efficient and inexpensive way to extract DNA for various next-generation sequencing applications including SPET and Nanopore among others.This research has been funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 677379 (Linking genetic resources, genomes and phenotypes of Solanaceous crops; G2P-SOL). David Alonso is grateful to Universitat Politecnica de Valencia for a predoctoral (PAID-01-16) contract under the Programa de Ayudas de Investigacion y Desarrollo initiative. Mariola Plazas is grateful to Generalitat Valenciana and Fondo Social Europeo for a postdoctoral grant (APOSTD/2018/014). Pietro Gramazio is grateful to Japan Society for the Promotion of Science for a Postdoctoral Grant (P19105, FY2019 JSPS Postdoctoral Fellowship for Research in Japan (Standard)). The Spanish Ministerio de Educacion, Cultura y Deporte funded a predoctoral fellowship granted to Edgar Garcia-Fortea (FPU17/02389).Vilanova Navarro, S.; Alonso-Martín, D.; Gramazio, P.; Plazas Ávila, MDLO.; García-Fortea, E.; Ferrante, P.; Schmidt, M.... (2020). SILEX: a fast and inexpensive high-quality DNA extraction method suitable for multiple sequencing platforms and recalcitrant plant species. Plant Methods. 16(1):1-11. https://doi.org/10.1186/s13007-020-00652-yS111161Scheben A, Batley J, Edwards D. Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J. 2017;15:149–61.Jung H, Winefield C, Bombarely A, Prentis P, Waterhouse P. Tools and strategies for long-read sequencing and de novo assembly of plant genomes. Trends Plant Sci. 2019;24:700–24.Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011;6:e19379.Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, Johnson EA. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS ONE. 2008;3:e3376.Scaglione D, Pinosio S, Marroni F, Centa E, Fornasiero A, Magris G, Scalabrin S, Cattonaro F, Taylor G, Morgante M. Single primer enrichment technology as a tool for massive genotyping: a benchmark on black poplar and maize. Ann Bot. 2019;124:543–51.Barchi L, Acquadro A, Alonso D, Aprea G, Bassolino L, Demurtas O, Ferrante P, Gramazio P, Mini P, Portis E, Scaglione D, Toppino L, Vilanova S, Díez MJ, Rotino G, Lanteri S, Prohens J, Giuliano G. Single primer enrichment technology (SPET) for high-throughput genotyping in tomato and eggplant germplasm. Front Plant Sci. 2019;10:1005.Vaillancourt B, Buell CR. High molecular weight DNA isolation method from diverse plant species for use with Oxford Nanopore sequencing. bioRxiv. 2019;1:783159.Anderson CB, Franzmayr BK, Hong SW, Larking AC, van Stijn TC, Tan R, Moraga R, Faville M, Griffiths A. Protocol: a versatile, inexpensive, high-throughput plant genomic DNA extraction method suitable for genotyping-by-sequencing. Plant Methods. 2018;14:75.Rana MM, Aycan M, Takamatsu T, Kaneko K, Mitsui T, Itoh K. Optimized nuclear pellet method for extracting next-generation sequencing quality genomic DNA from fresh leaf tissue. Methods Protoc. 2019;2:54.Doyle JJ, Doyle JL. Isolation of plant DNA from fresh tissue. Focus. 1990;12:13–5.Healey A, Furtado A, Cooper T, Henry RJ. Protocol: a simple method for extracting next-generation sequencing quality genomic DNA from recalcitrant plant species. Plant Methods. 2014;10:21.Martínez-González CR, Ramírez-Mendoza R, Jiménez-Ramírez J, Gallegos-Vázquez C, Luna-Vega I. Improved method for genomic DNA extraction for Opuntia Mill. (Cactaceae). Plant Methods. 2017;13:82.Barbier FF, Chabikwa TG, Ahsan MU, Cook SE, Powell R, Tanurdzic M, Beveridge C. A phenol/chloroform-free method to extract nucleic acids from recalcitrant, woody tropical species for gene expression and sequencing. Plant Methods. 2019;15:62.Souza DC, Teixeira TA. A simple and effective method to obtain high DNA quality and quantity from Cerrado plant species. Mol Biol Rep. 2019;46:4611–5.Kovačević N. Magnetic beads based nucleic acid purification for molecular biology applications. Sample preparation techniques for soil, plant, and animal samples. In: Micic M, editor. Springer Protoc Handb. 2016;53–67.Martin SL, Parent JS, Laforest M, Page E, Kreiner JM, James T. Population genomic approaches for weed science. Plants. 2019;8:354.Zhou Y, Zhang Y, He W, Wang J, Peng F, Huang L, Zhao S, Deng W. Rapid regeneration and reuse of silica columns from PCR purification and gel extraction kits. Sci Rep. 2018;8:12870.Park HJ, Cho H, Jung HS, Cho BH, Lee MY. Development of a DNA isolation device using poly(3,4-dihydroxy-l-phenylalanine)-coated swab for on-site molecular diagnostics. Sci Rep. 2019;9:8144.Boom R, Sol CJ, Salimans MM, Jansen CL, Wertheim-van Dillen PM, van der Noordaa J. Rapid and simple method for purification of nucleic acids. J Clin Microbiol. 1990;28:495–503.Carter MJ, Milton ID. An inexpensive and simple method for DNA purifications on silica particles. Nucleic Acids Res. 1993;21:1044.Carvalho J, Puertas G, Gaspar J, Azinheiro S, Diéguez L, Garrido-Maestu A, Vázquez M, Barros-Velázquez J, Cardoso S, Padro M. Highly efficient DNA extraction and purification from olive oil on a washable and reusable miniaturized device. Anal Chim Acta. 2018;1020:30–40.Branton D, Deamer D, Quick J, Loman NJ. DNA extraction strategies for nanopore sequencing. Nanopore Seq. World Sci. 2019;1:91–105.Cheng H, Zhang K, Libera J, De La Cruz M, Bedzyk M. Polynucleotide adsorption to negatively charged surfaces in divalent salt solutions. Biophys J. 2016;90:1164–74.Shi B, Shin Y, Hassanali A, Singer S. DNA Binding to the Silica Surface. J Phys Chem B. 2015;119:11030–40.Katevatis C, Fan A, Klapperich CM. Low concentration DNA extraction and recovery using a silica solid phase. PLoS ONE. 2017;12:e0176848.Green MR, Sambrook J. Isolation and quantification of DNA. Cold Spring Harb Protoc. 2018;2018:403–14.Toole K, Roffey P, Young E, Cho K, Shaw T, Smith M, Blagojevic N. Evaluation of commercial forensic DNA extraction kits for decontamination and extraction of DNA from biological samples contaminated with radionuclides. Forensic Sci Int. 2019;302:109867.Piskata Z, Servusova E, Babak V, Nesvadbova M, Borilova G. The quality of DNA isolated from processed food and feed via different extraction procedures. Molecules. 2019;24:1188.Xia Y, Chen F, Du Y, Liu C, Bu G, Xin Y, Boye L. A modified SDS-based DNA extraction method from raw soybean. Biosci Rep. 2019;39:2.Akkurt M. Comparison between modified DNA extraction protocols and commercial isolation kits in grapevine (Vitis vinifera L.). Genet Mol Res. 2012;11:2343–51.Marsal G, Baiges I, Canals JM, Zamora F, Fort F. A Fast, efficient method for extracting DNA from leaves, stems, and seeds of Vitis vinifera L. Am J Enol Vitic. 2011;62:376–81.Abdel-Latif A, Osman G. Comparison of three genomic DNA extraction methods to obtain high DNA quality from maize. Plant Methods. 2017;13:1.Huang J, Ge X, Sun M. Modified CTAB protocol using a silica matrix for isolation of plant genomic DNA. Biotechniques. 2000;28:432–4.Rogstad SH. Plant DNA extraction using silica. Plant Mol Biol Report. 2012;21:463.Li J-F, Li L, Sheen J. Protocol: a rapid and economical procedure for purification of plasmid or plant DNA with diverse applications in plant biology. Plant Methods. 2010;6:1.Li J-F, Sheen J. DNA purification from multiple sources in plant research with homemade silica resins. Humana Press. 2012;862:53–9.Vandeventer PE, Lin JS, Zwang TJ, Nadim A, Johal MS, Niemz A. Multiphasic DNA adsorption to silica surfaces under varying buffer, pH, and ionic strength conditions. J Phys Chem B. 2012;116:5661–70.Boesenberg-Smith KA, Pessarakli MM, Wolk DM. Assessment of DNA yield and purity: an overlooked detail of PCR troubleshooting. Clin Microbiol Newsl. 2012;34:1–6.Emaus MN, Clark KD, Hinners P, Anderson JL. Preconcentration of DNA using magnetic ionic liquids that are compatible with real-time PCR for rapid nucleic acid quantification. Anal Bioanal Chem. 2018;410:4135–44.Dumschott K, Schmidt MHW, Chawla HS, Snowdon R, Usadel B. Oxford Nanopore sequencing: new opportunities for plant genomics? J Exp Bot. 2020;eraa263Knapp S, Sagona E, Carbonell AKZ, Chiarini F. A revision of the Solanum elaeagnifolium clade (Elaeagnifolium clade; subgenus Leptostemonum, Solanaceae). PhytoKeys. 2017;84:1–104.García-Fortea E, Gramazio P, Vilanova S, Fita A, Mangino G, Villanueva G, Arrones A, Knapp S, Prohens J, Plazas M. First successful backcrossing towards eggplant (Solanum melongena) of a New World species, the silverleaf nightshade (S. elaeagnifolium), and characterization of interspecific hybrids and backcrosses. Sci Hort. 2019;246:563–73.Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat. 1996;5:3299–314.Wickham H. ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag; 2016.Ponti G, Maccaferri M, Manfredini M, Kaleci S, Mandrioli M, Pellacani G, Ozben T, Depenni R, Bianchi G, Pirola G, Tomasi A. The value of fluorimetry (Qubit) and spectrophotometry (NanoDrop) in the quantification of cell-free DNA (cfDNA) in malignant melanoma and prostate cancer patients. Clin Chim Acta. 2018;479:14–9.Lakshmi R, Baskar V, Ranga U. Extraction of superior-quality plasmid DNA by a combination of modified alkaline lysis and silica matrix. Anal Biochem. 1999;272:109–12.Taylor JI, Hurst CD, Davies MJ, Sachsinger N, Bruce IJ. Application of magnetite and silica–magnetite composites to the isolation of genomic DNA. J Chromatogr A. 2000;890:159–66.Prodělalová J, Rittich B, Španová A, Petrová K, Beneš MJ. Isolation of genomic DNA using magnetic cobalt ferrite and silica particles. J Chromatogr A. 2004;1056:43–8.Shan Z, Jiang Y, Guo M, Bennett JC, Li X, Tian H, Oakes K, Zhang, Zhou Y, Huang Q, Chen H. Promoting DNA loading on magnetic nanoparticles using a DNA condensation strategy. Colloids Surfaces B Biointerfaces. 2015;125:247–54.Greco M, Sáez C, Brown M, Bitonti M. A simple and effective method for high quality co-extraction of genomic DNA and total RNA from low biomass Ectocarpus siliculosus, the model brown alga. PLoS ONE. 2014;9:e96470.Schrader C, Schielke A, Ellerbroek L, Johne R. PCR inhibitor – occurrence, properties and removal. J Appl Microbiol. 2012;113:1014–26.Demeke T, Adams RP. The effects of plant polysaccharides and buffer additives on PCR. Biotechniques. 1992;12:332–4.Asami DK, Hong YJ, Barrett DM, Mitchell AE. Comparison of the total phenolic and ascorbic acid content of freeze-dried and air-dried marionberry, strawberry, and corn grown using conventional, organic, and sustainable agricultural practices. J Agric Food Chem. 2003;51:1237–41.Schmidt M, Vogel A, Denton A, Istace B, Wormit A, van de Geest H, Bolger M, Alseekh S, Maß J, Pfaff C, Schurr U, Chetelat R, Maumus F, Aury J, Koren S, Fernie A, Zamir D, Bolger A, Usadel B. De novo assembly of a new Solanum pennellii accession using nanopore sequencing. Plant cell. 2017;29:2336–48

Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants

[EN] Simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers are amongst the most common markers of choice for studies of diversity and relationships in horticultural species. We have used 11 SSR and 35 SNP markers derived from transcriptome sequencing projects to fingerprint 48 accessions of a collection of brinjal (Solanum melongena), gboma (S. macrocarpon) and scarlet (S. aethiopicum) eggplant complexes, which also include their respective wild relatives S. incanum, S. dasyphyllum and S. anguivi. All SSR and SNP markers were polymorphic and 34 and 36 different genetic fingerprints were obtained with SSRs and SNPs, respectively. When combining both markers all accessions but two had different genetic profiles. Although on average SSRs were more informative than SNPs, with a higher number of alleles, genotypes and polymorphic information content (PIC), and expected heterozygosity (He) values, SNPs have proved highly informative in our materials. Low observed heterozygosity (Ho) and high fixation index (f) values confirm the high degree of homozygosity of eggplants. Genetic identities within groups of each complex were higher than with groups of other complexes, although differences in the ranks of genetic identity values among groups were observed between SSR and SNP markers. For low and intermediate values of pair-wise SNP genetic distances, a moderate correlation between SSR and SNP genetic distances was observed (r(2) = 0.592), but for high SNP genetic distances the correlation was low (r(2) = 0.080). The differences among markers resulted in different phenogram topologies, with a different eggplant complex being basal (gboma eggplant for SSRs and brinjal eggplant for SNPs) to the two others. Overall the results reveal that both types of markers are complementary for eggplant fingerprinting and that interpretation of relationships among groups may be greatly affected by the type of marker used.This work has been funded by European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops) and by Spanish Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (Grant AGL2015-64755-R from MINECO/FEDER). Pietro Gramazio is grateful to Universitat Politecnica de Valencia for a pre-doctoral contract (Programa FPI de la UPV-Subprograma 1/2013 call). Mariola Plazas is grateful to Spanish Ministerio de Economia, Industria y Competitividad for a post-doctoral grant within the Juan de la Cierva-Formacion programme (FJCI-2015-24835).Gramazio, P.; Prohens Tomás, J.; Borras, D.; Plazas Ávila, MDLO.; Herraiz García, FJ.; Vilanova Navarro, S. (2017). Comparison of transcriptome-derived simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for genetic fingerprinting, diversity evaluation, and establishment of relationships in eggplants. Euphytica. 213(264):1-18. https://doi.org/10.1007/s10681-017-2057-3S118213264Acquadro A, Barchi L, Gramazio P et al (2017) Coding SNPs analysis highlights genetic relationships and evolution pattern in eggplant complexes. PLoS ONE 12:e0180774. https://doi.org/10.1371/journal.pone.0180774Adeniji O, Kusolwa P, Reuben S (2013) Morphological descriptors and micro satellite diversity among scarlet eggplant groups. Afr Crop Sci J 21(1):37–49Aguoru C, Omoigui L, Olasan J (2015) Molecular characterization of Solanum species (Solanum aethiopicum complex; Solanum macrocarpon and Solanum anguivi) using multiplex RAPD primers. J Plant Stud 4:27–34. https://doi.org/10.5539/jps.v4n1p27Arumuganathan K, Earle E (1991) Nuclear DNA content of some important plant species. Plant Mol Biol Rep 9(3):208–218Ashrafi H, Hill T, Stoffel K et al (2012) De novo assembly of the pepper transcriptome (Capsicum annuum): a benchmark for in silico discovery of SNPs, SSRs and candidate genes. BMC Genom 13:1–15. https://doi.org/10.1186/1471-2164-13-571Augustinos AA, Petropoulos C, Karasoulou V et al (2016) Assessing diversity among traditional Greek and foreign eggplant cultivars using molecular markers and morphometrical descriptors. Span J Agric Res 14:e0710. https://doi.org/10.5424/sjar/2016144-9020Avise JC (2012) Molecular markers, natural history and evolution. Springer Science & Business Media, Berlin. https://doi.org/10.1007/978-1-4615-2381-9Blanca J, Cañizares J, Roig C et al (2011) Transcriptome characterization and high throughput SSRs and SNPs discovery in Cucurbita pepo (Cucurbitaceae). BMC Genom 12:104. https://doi.org/10.1186/1471-2164-12-104Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314–331Bukenya Z, Carasco J (1994) Biosystematic study of Solanum macrocarpon—S. dasyphyllum complex in Uganda and relations with Solanum linnaeanum. East Afr Agric For J 59(3):187–204Castillo A, Budak H, Varshney RK et al (2008) Transferability and polymorphism of barley EST-SSR markers used for phylogenetic analysis in Hordeum chilense. BMC Plant Biol 8:97. https://doi.org/10.1186/1471-2229-8-97Choudhary S, Sethy NK, Shokeen B, Bhatia S (2009) Development of chickpea EST-SSR markers and analysis of allelic variation across related species. Theor Appl Genet 118:591–608. https://doi.org/10.1007/s00122-008-0923-zCoates BS, Sumerford DV, Miller NJ et al (2009) Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. J Hered 100:556–564. https://doi.org/10.1093/jhered/esp028D’Agostino N, Golas T, van de Geest H et al (2013) Genomic analysis of the native European Solanum species, S. dulcamara. BMC Genom 14:356. https://doi.org/10.1186/1471-2164-14-356Daunay MC, Hazra P (2012) Eggplant. In: Peter KV, Hazra P (eds) Handbook of Vegetables. Studium Press, Houston, pp 257–322Davey J, Hohenlohe P, Etter P et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510. https://doi.org/10.1038/nrg3012De Barba M, Miquel C, Lobréaux S et al (2016) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Mol Ecol Resour 17(3):492–507. https://doi.org/10.1111/1755-0998.12594Doyle J, Doyle J (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull 19:11–15Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5:435–445. https://doi.org/10.1038/nrg1348Felsenstein, J (2007). PHYLIP (Phylogeny Inference Package) Version 3.67. Department of Genome Sciences, University of Washington, Seattle, WA, USAFernandez-Silva I, Whitney J, Wainwright B (2013) Microsatellites for next-generation ecologists: a post-sequencing bioinformatics pipeline. PLoS ONE 8(2):e55990Filippi CV, Aguirre N, Rivas JG et al (2015) Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol 15:52. https://doi.org/10.1186/s12870-014-0360-xFischer MC, Rellstab C, Leuzinger M et al (2017) Estimating genomic diversity and population differentiation—an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genom 18:69. https://doi.org/10.1186/s12864-016-3459-7Furini A, Wunder J (2004) Analysis of eggplant (Solanum melongena)-related germplasm: morphological and AFLP data contribute to phylogenetic interpretations and germplasm utilization. Theor Appl Genet 108:197–208. https://doi.org/10.1007/s00122-003-1439-1Gadaleta A, Giancaspro A, Zacheo S et al (2011) Comparison of genomic and EST-derived SSR markers in phylogenetic analysis of wheat. Plant Genet Resour 9:243–246. https://doi.org/10.1017/S147926211100030XGe H, Liu Y, Jiang M et al (2013) Analysis of genetic diversity and structure of eggplant populations (Solanum melongena L.) in China using simple sequence repeat markers. Sci Hortic 162:71–75. https://doi.org/10.1016/j.scienta.2013.08.004Gonzaga ZJ (2015) Evaluation of SSR and SNP Markers for Molecular Breeding in Rice. Plant Breed Biotechnol 3:139–152. https://doi.org/10.9787/PBB.2015.3.2.139Goodwin S, McPherson J, McCombie W (2016) Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 17(6):333–351Gramazio P, Blanca J, Ziarsolo P et al (2016) Transcriptome analysis and molecular marker discovery in Solanum incanum and S. aethiopicum, two close relatives of the common eggplant (Solanum melongena) with interest for breeding. BMC Genom 17:300. https://doi.org/10.1186/s12864-016-2631-4Grover A, Sharma PC (2014) Development and use of molecular markers: past and present. Crit Rev Biotechnol 8551:1–13. https://doi.org/10.3109/07388551.2014.959891Hamblin MT, Warburton ML, Buckler ES (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS ONE 2:e1367. https://doi.org/10.1371/journal.pone.0001367Hess JE, Matala AP (2011) Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Mol Ecol Resour. https://doi.org/10.1111/j.1755-0998.2010.02958.xHighton R (1993) The relationship between the number of loci and the statistical support for the topology of UPGMA trees obtained from genetic distance data. Mol Phylogenet Evol 2:337–343Hirakawa H, Shirasawa K, Miyatake K, Nunome, T et al (2014) Draft genome sequence of eggplant (Solanum melongena L.): the representative solanum species indigenous to the old world. DNA Res 21:649–660. https://doi.org/10.1093/dnares/dsu027Hong CP, Piao ZY, Kang TW et al (2007) Genomic distribution of simple sequence repeats in Brassica rapa. Mol Cells 23:349–356.Hu J, Wang L, Li J (2011) Comparison of genomic SSR and EST-SSR markers for estimating genetic diversity in cucumber. Biol Plant 55:577–580. https://doi.org/10.1007/s10535-011-0129-0Isshiki S, Iwata N, Khan MMR (2008) ISSR variations in eggplant (Solanum melongena L.) and related Solanum species. Sci Hortic 117:186–190. https://doi.org/10.1016/j.scienta.2008.04.003Jones ES, Sullivan H, Bhattramakki D, Smith JSC (2007) A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor Appl Genet 115:361–371. https://doi.org/10.1007/s00122-007-0570-9Kalia RK, Rai MK, Kalia S et al (2011) Microsatellite markers: an overview of the recent progress in plants. Euphytica 177:309–334Kashi Y, King DG (2006) Simple sequence repeats as advantageous mutators in evolution. Trends Genet 22:253–259. https://doi.org/10.1016/j.tig.2006.03.005Kaushik P, Prohens J, Vilanova S et al (2016) Phenotyping of eggplant wild relatives and interspecific hybrids with conventional and phenomics descriptors provides insight for their potential utilization in breeding. Front Plant Sci 7:677Kim C, Guo H, Kong W et al (2016) Application of genotyping by sequencing technology to a variety of crop breeding programs. Plant Sci 242:14–22Knapp S, Vorontsova MS, Prohens J (2013) Wild relatives of the eggplant (Solanum melongena L.: Solanaceae): new understanding of species names in a complex group. PLoS ONE 8:e57039Kruglyak S, Durrett RT, Schug MD, Aquadro CF (1998) Equilibrium distributions of microsatellite repeat length resulting from a balance between slippage events and point mutations. Proc Natl Acad Sci USA 95:10774–10778. https://doi.org/10.1073/pnas.95.18.10774Lester RN, Daunay MC (2003) Diversity of African vegetable Solanum species and its implications for a better understanding of plant domestication. Schriften zu Genetischen Ressourcen 22:137–152Lester RN, Niakan L (1986) Origin and domestication of the scarlet eggplant, Solanum aethiopicum, from S. anguivi in Africa. In: D’Arcy WG (ed) Solanaceae: biology and systematics. Columbia University Press, New York, pp 433–456Lester RN, Jaeger PML, Bleijendaal-Spierings BHM et al (1990) African eggplants-a review of collecting in West Africa. Plant Genet Resour Newsl 81:17–26Levin R, Myers N, Bohs L (2006) Phylogenetic relationships among the ‘spiny solanums’ (Solanum subgenus Leptostemonum, Solanaceae). Am J Bot 93(1):157–169Li WH, Gojobori T, Nei M (1981) Pseudogenes as a paradigm of neutral evolution. Nature 292:237–239Li YC, Korol AB, Fahima T et al (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11:2453–2465Liu K, Muse S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220. https://doi.org/10.1038/214637b0Martínez-Arias R, Calafell F, Mateu E et al (2001) Sequence variability of a human pseudogene. Genome Res 11:1071–1085. https://doi.org/10.1101/gr.167701Meyer RS, Karol KG, Little DP et al (2012) Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Mol Phylogenet Evol 63:685–701. https://doi.org/10.1016/j.ympev.2012.02.006Muñoz-Falcón J, Prohens J, Vilanova S, Nuez F (2009) Diversity in commercial varieties and landraces of black eggplants and implications for broadening the breeders’ gene pool. Ann Appl Biol 154(3):453–465Nandha PS, Singh J (2014) Comparative assessment of genetic diversity between wild and cultivated barley using gSSR and EST-SSR markers. Plant Breed 133:28–35. https://doi.org/10.1111/pbr.12118Nei M (1972) Genetic distance between populations. Am Nat 106:283–292. https://doi.org/10.1086/282771Nunome T, Negoro S, Kono I et al (2009) Development of SSR markers derived from SSR-enriched genomic library of eggplant (Solanum melongena L.). Theor Appl Genet 119:1143–1153. https://doi.org/10.1007/s00122-009-1116-0Page R (2001) TreeView. Glasgow University, GlasgowPeakall P, Smouse R (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research an update. Bioinformatics 28:2537–2539Pessarakli M, Dris R (2004) Pollination and breeding of eggplants. J Food Agric Environ 2:218–219Plazas M, Andújar I, Vilanova S et al (2014) Conventional and phenomics characterization provides insight into the diversity and relationships of hypervariable scarlet (Solanum aethiopicum L.) and gboma (S. macrocarpon L.) eggplant complexes. Front. Plant Sci 5:318Ranil R, Niran H, Plazas M et al (2015) Improving seed germination of the eggplant rootstock Solanum torvum by testing multiple factors using an orthogonal array design. Sci Hortic 193:174–181. https://doi.org/10.1016/j.scienta.2015.07.030Sakata Y, Lester RN (1997) Chloroplast DNA diversity in brinjal eggplant (Solanum melongena L.) and related species. Euphytica 97:295–301. https://doi.org/10.1023/A:1003000612441Sakata Y, Nishio T, Matthews PJ (1991) Chloroplast DNA analysis of eggplant (Solanum melongena) and related species for their taxonomic affinity. Euphytica 55:21–26Särkinen T, Bohs L, Olmstead RG, Knapp S (2013) A phylogenetic framework for evolutionary study of the nightshades (Solanaceae): a dated 1000-tip tree. BMC Evol Biol 13:214. https://doi.org/10.1186/1471-2148-13-214Scheben A, Batley J, Edwards D (2017) Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application. Plant Biotechnol J 15:149–161Sneath P, Sokal R (1973) Numerical taxonomy. The principles and practice of numerical classification. W H Freeman Limited, San FranciscoStàgel A, Portis E, Toppino L et al (2008) Gene-based microsatellite development for mapping and phylogeny studies in eggplant. BMC Genom 9:357. https://doi.org/10.1186/1471-2164-9-357Sunseri F, Polignano GB, Alba V et al (2010) Genetic diversity and characterization of African eggplant germplasm collection. Afr J Plant Sci 4:231–241Syfert MM, Castañeda-Álvarez NP, Khoury CK et al (2016) Crop wild relatives of the brinjal eggplant (Solanum melongena): poorly represented in genebanks and many species at risk of extinction. Am J Bot 103:635–651. https://doi.org/10.3732/ajb.1500539Thiel T, Michalek W, Varshney R, Graner A (2003) Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet 106:411–422. https://doi.org/10.1007/s00122-002-1031-0Thomson MJ, Alfred J, Dangl J et al (2014) High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2:195–212. https://doi.org/10.9787/PBB.2014.2.3.195Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. https://doi.org/10.1093/bib/bbs017Tumbilen Y, Frary A, Daunay MC, Doganlar S (2011) Application of EST-SSRs to examine genetic diversity in eggplant and its close relatives. Turk J Biol 35:125–136. https://doi.org/10.3906/biy-0906-57van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120:1289–1299. https://doi.org/10.1007/s00122-009-1256-2Van Tassell CP, Smith TPL, Matukumalli LK et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5:247–252. https://doi.org/10.1038/nmeth.1185Varshney R, Graner A, Sorrells M (2005) Genic microsatellite markers in plants: features and applications. Trends Biotechnol 23(1):48–55Varshney RK, Chabane K, Hendre PS et al (2007) Comparative assessment of EST-SSR, EST-SNP and AFLP markers for evaluation of genetic diversity and conservation of genetic resources using wild, cultivated and elite barleys. Plant Sci 173:638–649. https://doi.org/10.1016/j.plantsci.2007.08.010Vilanova S, Manzur JP, Prohens J (2012) Development and characterization of genomic simple sequence repeat markers in eggplant and their application to the study of diversity and relationships in a collection of different cultivar types and origins. Mol Breed 30:647–660. https://doi.org/10.1007/s11032-011-9650-2Vilanova S, Hurtado M, Cardona A (2014) Genetic diversity and relationships in local varieties of eggplant from different cultivar groups as assessed by genomic SSR markers. Not Bot Horti Agrobo Cluj-Napoca 42:59–65Vogel JP, Gu YQ, Twigg P et al (2006) EST sequencing and phylogenetic analysis of the model grass Brachypodium distachyon. Theor Appl Genet 113:186–195. https://doi.org/10.1007/s00122-006-0285-3Vorontsova MS, Stern S, Bohs L, Knapp S (2013) African spiny solanum (subgenus Leptostemonum, Solanaceae): a thorny phylogenetic tangle. Bot J Linn Soc 173:176–193. https://doi.org/10.1111/boj.12053Weese TL, Bohs L (2010) Eggplant origins: out of Africa, into the Orient. Taxon 59:49–56. https://doi.org/10.2307/27757050Wright S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19:395–420. https://doi.org/10.2307/2406450Xiao M, Zhang Y, Chen X et al (2013) Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J Biotechnol 166:122–134. https://doi.org/10.1016/j.jbiotec.2013.04.004Yan J, Yang X, Shah T et al (2010) High-throughput SNP genotyping with the Goldengate assay in maize. Mol Breed 25:441–451. https://doi.org/10.1007/s11032-009-9343-2Yang X, Xu Y, Shah T et al (2011) Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetica 139:1045–1054. https://doi.org/10.1007/s10709-011-9606-9Yu J, Zhang Z, Zhu C et al (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2:63. https://doi.org/10.3835/plantgenome2008.09.0009Zhan L, Paterson I, Fraser B (2016) MEGASAT: automated inference of microsatellite genotypes from sequence data. Ecol Resour, Mol. https://doi.org/10.1111/1755-0998.1256

New ABA-Hypersensitive Arabidopsis Mutants Are Affected in Loci Mediating Responses to Water Deficit and Dickeya dadantii Infection

On water deficit, abscisic acid (ABA) induces stomata closure to reduce water loss by transpiration. To identify Arabidopsis thaliana mutants which transpire less on drought, infrared thermal imaging of leaf temperature has been used to screen for suppressors of an ABA-deficient mutant (aba3-1) cold-leaf phenotype. Three novel mutants, called hot ABA-deficiency suppressor (has), have been identified with hot-leaf phenotypes in the absence of the aba3 mutation. The defective genes imparted no apparent modification to ABA production on water deficit, were inherited recessively and enhanced ABA responses indicating that the proteins encoded are negative regulators of ABA signalling. All three mutants showed ABA-hypersensitive stomata closure and inhibition of root elongation with little modification of growth and development in non-stressed conditions. The has2 mutant also exhibited increased germination inhibition by ABA, while ABA-inducible gene expression was not modified on dehydration, indicating the mutated gene affects early ABA-signalling responses that do not modify transcript levels. In contrast, weak ABA-hypersensitivity relative to mutant developmental phenotypes suggests that HAS3 regulates drought responses by both ABA-dependent and independent pathways. has1 mutant phenotypes were only apparent on stress or ABA treatments, and included reduced water loss on rapid dehydration. The HAS1 locus thus has the required characteristics for a targeted approach to improving resistance to water deficit. In contrast to has2, has1 exhibited only minor changes in susceptibility to Dickeya dadantii despite similar ABA-hypersensitivity, indicating that crosstalk between ABA responses to this pathogen and drought stress can occur through more than one point in the signalling pathway