Untying the Gordian Knot of plastid phylogenomic conflict: a case from ferns
Creators: name: Wang, Ting
Publisher: name: Zenodo
Year: 2021

description: Phylogenomic studies based on plastid genome have resolved the recalcitrant relationships among various plants, yet the phylogeny of Dennstaedtiaceae at the taxonomic level remains unresolved due to conflicting plastid genes, limited molecular data and incomplete taxon sampling of previous studies. The present study generated 31 new plastid genomes of Dennstaedtiaceae (9 genera, 30 species) and combined 41 publicly available sequences of plastid genome (including 24 families, 26 genera, 41 species) to solve and explore the evolution of Dennstaedtiaceae. In order to minimize the impact of systematic errors on the resolution of phylogenetic inference, we applied six strategies to generate 30 datasets based on CDS, Spacer, and All datasets, and two tree inference methods (maximum-likelihood, ML; and multispecies coalescent, MSC) to comprehensively analyze the plastome-scale data. Besides, the phylogenetic signal among all loci was quantified for the controversial node using the ML framework, and the phylogenetic hypotheses among all datasets were tested. In the species tree based on different data sets and methods, obvious conflicts were detected at the base of the polypod ferns. Meanwhile, the topology of the "CDS-codon-align-rm3" (CDS removed the third codon) matrix was selected as the primary reference or summary tree due to its analysis results are consistent, and similar to the topological structure of the amino-acid matrix. The final phylogenetic tree supported Dennstaedtiaceae as the sister group to eupolypods, and<em> Dennstaedtia</em> (sen. lat.) can divided into smaller genera, which was also supported by geographical distribution and plastid structure. This robust reconstructed phylogenetic backbone established a framework for future studies on Dennstaedtiaceae classification, evolution and diversification. The present study suggests considering plastid phylogenomic conflict when using plastid genomes. From our results, reducing saturated genes or sites can effectively mitigate the tree conflicts of distantly related taxa. Moreover, amino acid sequences may verify the accuracy of nucleotide-based phylogeny.
description: Funding provided by: The systematic classification of the genus Microlepia*<br>Crossref Funder Registry ID: <br>Award Number: 31370234
description: <i>Taxon Sampling and Sequencing</i> In this study, 31 new Dennstaedtiaceae plastomes belonging to 30 species of 9 genera were sequenced. Combined with publicly available complete plastome data of NCBI, plastome from 72 species of 47 genera and 25 families were analyzed. Detailed information on the studied taxa, arranged according to the PPG I (2016) classification, is provided in Supplementary Table S1. Total DNA was extracted from fresh, young leaves using Plant Mini Kit (Qiagen, CA, USA), following the manufacturer's protocol. DNA quality and integrity were evaluated on 1% agarose gels. DNA purity was determined with the NanoPhotometer® spectrophotometer (Implen, CA, USA), and DNA concentration was measured using the Qubit® DNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, CA, USA). Sequencing libraries were generated using the NEB Next® Ultra DNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer's recommendations. Approximately 700 ng of high-quality DNA per sample was used for library preparation; index codes were added to attribute sequences to each sample. Briefly, the chip DNA was purified using the AMPure XP system (Beckman Coulter, Beverly, USA), and DNA fragments of a specified length were selected from the agarose gels. Subsequently, the size-selected, adaptor-ligated DNA was incubated with 3 µL of USER Enzyme (Uracil-Specific Excision Reagent; NEB, USA) at 37 °C for 15 min followed by 5 min at 95 °C. Then, PCR was performed with Phusion High-Fidelity DNA polymerase (Thermo Fisher Scientific), using the universal primers plus one index (X) primer. Finally, the PCR products were purified (AMPure XP system), and the quality was assessed on an Agilent Bioanalyzer 2100 system. Subsequently, the clustering of the index-coded samples was performed on a cBot Cluster Generation System using the HiSeq 4000 PE Cluster Kit (Illumina) according to the manufacturer's instructions. After cluster generation, the libraries were sequenced on an Illumina HiSeq 4000 platform, and 150-bp paired-end reads were generated (Yan et al., 2013). The raw data were filtered using the cluster based on default parameter (-L 5, -p 0.5, -N 0.1). High N rate reads (N content &gt; 10%) and low-quality reads (50% of bases with Q ≤ 5) were removed from the raw data to obtain clean data. <i>Plastid Genome</i><i> Assembly, Annotation and Comparison</i> The paired-end reads were filtered following the GetOrganelle pipe-line (https://github.com/Kinggerm/GetOrganelle) to obtain plastid-like reads. The filtered reads were assembled by SPAdes v3.10 (Bankevich et al. 2012), and the final "fastq" files were filtered using GetOrganelle to obtain pure plastid contigs. The filtered de Bruijn graphs were viewed and edited by Bandage (Wick et al. 2015), and finally, circular plastomes were obtained. The assembled plastomes were annotated with PGA (Plastid Genome Annotator) (Qu et al. 2019), using the published plastome of <i>Histiopteris incisa</i> (MH319942) as the reference. The results were manually aligned to the reference genome using Geneious v11.1.5 (Kearse et al. 2012) to confirm the plastomes. Finally, 31 high-quality, complete plastid genome sequences were obtained. OrganellarGenomeDRAW (OGDRAW) v1.3.1 (https://chlorobox.mpimp-golm.mpg.de/OGDraw.html) was used to visualize the structural features of the plastomes of 31 species (Greiner et al. 2019). <i>Sequence Alignment and Cleanup</i> Furthermore, the "get_annotated_regions_from_gb.py" (https://github.com/Kinggerm/PersonalUtilities/) script was used to automatically extract all CDS (coding regions) and intergenic spacer regions from a list of annotated files in GenBank-format and manually calibrate the results. The CDS and intergenic spacer regions were individually aligned using the L-INS-i method of MAFFT (Multiple Alignment using Fast Fourier Transform) v.7.475 (Katoh et al. 2002). Further, loci covering less than 55% of the species, and loci of CDS less than 100 bp or intergenic spacer regions less than 50 bp were removed to minimize the use of loci with limited information or present in relatively few species. Finally, 166 loci, including 81 CDS and 85 intergenic spacer regions, were obtained from 71 plastomes for downstream analysis. <i>Datasets Generation</i> The script "concatenate_fasta.py" (https://github.com/Kinggerm/PersonalUtilities/) was used to concatenate the alignments of each locus and create three basic datasets, the CDS, the Spacer (intergenic spacer regions), and the All (the concatenated CDS and Spacer) datasets. Furthermore, four strategies were applied to reduce the systematic error in the three basic datasets. The first strategy excluded ambiguously aligned regions using Gblocks v0.91b (Castresana 2000) with relaxed, default and strict parameters ("Allowed Gap Positions" = "With All/Half/None"), producing the "CDS-GB-all/half/none", "Spacer-GB-all/half/none", and "All-GB-all/half/none" datasets. The second and third strategies identified and excluded loci with high levels of excessive substitutional saturation (slope and <i>R<sup>2</sup></i> values) and evolutionary distance (long-branch score) using TreSpEx v.1.1 (Struck 2014). Then, the density plots of the long-branch score, slope and <i>R<sup>2</sup></i> values were generated with R v.3.2.2 (The R Core Team 2015). The distribution of the long-branch scores of CDS and Spacer showed a small shoulder at 0.45 and 0.90, respectively (Figs. S1a, d), corresponding to the removal of 13 loci of CDS and 9 loci of Spacer from CDS/Spacer/All datasets to form "CDS-LB", "Spacer-LB", and "All-LB" datasets. The 22 loci of CDS (0.50) and 18 loci of Spacer (0.344) located on the left "hump" of the <i>R<sup>2</sup></i> distribution (Figs. S1b, e) were trimmed from CDS/Spacer/All datasets to form the "CDS-<i>R<sup>2</sup></i>", "Spacer-<i>R<sup>2</sup></i>", and "All-<i>R<sup>2</sup></i>" datasets. Then, 9 loci of CDS (0.104) and 73 loci (0.30) of Spacer (Figs. S1c, f) located on the left "hump" of the slope distribution (Figs. S1b, c) were removed from CDS/Spacer/All datasets to form the "CDS-slope", "Spacer-slope", and "All-slope" datasets. The fourth strategy used TreSpEx v.1.1 (Struck 2014) to calculate the average bootstrap support (BS) of all nodes in the corresponding maximum likelihood tree generated from 166 loci (Table S2) and then removing loci with at least 75% ultrafast bootstrap (UFBoot) support, generating "CDS-BS75", "Spacer-BS75", and "All- BS75" datasets. The loci excluded from each dataset are listed in Supplementary Table S2.