@article{VenjakobRuedenauerKleinetal.2022,
  author    = {Venjakob, C. and Ruedenauer, F. A. and Klein, A.-M. and Leonhardt, S. D.},
  title     = {Variation in nectar quality across 34 grassland plant species},
  series = {Plant Biology},
  volume    = {24},
  journal   = {Plant Biology},
  number    = {1},
  doi       = {10.1111/plb.13343},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262612},
  pages     = {134 -- 144},
  year      = {2022},
  abstract  = {Floral nectar is considered the most important floral reward for attracting pollinators. It contains large amounts of carbohydrates besides variable concentrations of amino acids and thus represents an important food source for many pollinators. Its nutrient content and composition can, however, strongly vary within and between plant species. The factors driving this variation in nectar quality are still largely unclear. We investigated factors underlying interspecific variation in macronutrient composition of floral nectar in 34 different grassland plant species. Specifically, we tested for correlations between the phylogenetic relatedness and morphology of plants and the carbohydrate (C) and total amino acid (AA) composition and C:AA ratios of nectar. We found that compositions of carbohydrates and (essential) amino acids as well as C:AA ratios in nectar varied significantly within and between plant species. They showed no clear phylogenetic signal. Moreover, variation in carbohydrate composition was related to family-specific structural characteristics and combinations of morphological traits. Plants with nectar-exposing flowers, bowl- or parabolic-shaped flowers, as often found in the Apiaceae and Asteraceae, had nectar with higher proportions of hexoses, indicating a selective pressure to decelerate evaporation by increasing nectar osmolality. Our study suggests that variation in nectar nutrient composition is, among others, affected by family-specific combinations of morphological traits. However, even within species, variation in nectar quality is high. As nectar quality can strongly affect visitation patterns of pollinators and thus pollination success, this intra- and interspecific variation requires more studies to fully elucidate the underlying causes and the consequences for pollinator behaviour.},
  language  = {en}
}
@article{PliegerWolf2022,
  author    = {Plieger, Tanja and Wolf, Matthias},
  title     = {18S and ITS2 rDNA sequence-structure phylogeny of Prototheca (Chlorophyta, Trebouxiophyceae)},
  series = {Biologia},
  volume    = {77},
  journal   = {Biologia},
  number    = {2},
  issn      = {1336-9563},
  doi       = {10.1007/s11756-021-00971-y},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-269897},
  pages     = {569-582},
  year      = {2022},
  abstract  = {Protothecosis is an infectious disease caused by organisms currently classified within the green algal genus Prototheca. The disease can manifest as cutaneous lesions, olecranon bursitis or disseminated or systemic infections in both immunocompetent and immunosuppressed patients. Concerning diagnostics, taxonomic validity is important. Prototheca, closely related to the Chlorella species complex, is known to be polyphyletic, branching with Auxenochlorella and Helicosporidium. The phylogeny of Prototheca was discussed and revisited several times in the last decade; new species have been described. Phylogenetic analyses were performed using ribosomal DNA (rDNA) and partial mitochondrial cytochrome b (cytb) sequence data. In this work we use Internal Transcribed Spacer 2 (ITS2) as well as 18S rDNA data. However, for the first time, we reconstruct phylogenetic relationships of Prototheca using primary sequence and RNA secondary structure information simultaneously, a concept shown to increase robustness and accuracy of phylogenetic tree estimation. Using encoded sequence-structure data, Neighbor-Joining, Maximum-Parsimony and Maximum-Likelihood methods yielded well-supported trees in agreement with other trees calculated on rDNA; but differ in several aspects from trees using cytb as a phylogenetic marker. ITS2 secondary structures of Prototheca sequences are in agreement with the well-known common core structure of eukaryotes but show unusual differences in their helix lengths. An elongation of the fourth helix of some species seems to have occurred independently in the course of evolution.},
  language  = {en}
}
@article{MergetKoetschanHackletal.2012,
  author    = {Merget, Benjamin and Koetschan, Christian and Hackl, Thomas and F{\"o}rster, Frank and Dandekar, Thomas and M{\"u}ller, Tobias and Schultz, J{\"o}rg and Wolf, Matthias},
  title     = {The ITS2 Database},
  series = {Journal of Visual Expression},
  volume    = {61},
  journal   = {Journal of Visual Expression},
  number    = {e3806},
  doi       = {10.3791/3806},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-124600},
  year      = {2012},
  abstract  = {The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation. The ITS2 Database presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank accurately reannotated. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold. The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE and ProfDistS for multiple sequence-structure alignment calculation and Neighbor Joining tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure. In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.},
  language  = {en}
}