| 1. |
- Kang, Seungho, et al.
(author)
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The integrin-mediated adhesive complex in the ancestor of animals, fungi, and amoebae
- 2021
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In: Current Biology. - : Elsevier BV. - 0960-9822 .- 1879-0445. ; 31:14, s. 3-3085
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Journal article (peer-reviewed)abstract
- Integrins are transmembrane receptors that activate signal transduction pathways upon extracellular matrix binding. The integrin-mediated adhesive complex (IMAC) mediates various cell physiological processes. Although the IMAC was thought to be specific to animals, in the past ten years these complexes were discovered in other lineages of Obazoa, the group containing animals, fungi, and several microbial eukaryotes. Very recently, many genomes and transcriptomes from Amoebozoa (the eukaryotic supergroup sister to Obazoa), other obazoans, orphan protist lineages, and the eukaryotes’ closest prokaryotic relatives, have become available. To increase the resolution of where and when IMAC proteins exist and have emerged, we surveyed these newly available genomes and transcriptomes for the presence of IMAC proteins. Our results highlight that many of these proteins appear to have evolved earlier in eukaryote evolution than previously thought and that co-option of this apparently ancient protein complex was key to the emergence of animal-type multicellularity. The role of the IMACs in amoebozoans is unknown, but they play critical adhesive roles in at least some unicellular organisms.
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| 2. |
- Onsbring, Henning, et al.
(author)
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An efficient single-cell transcriptomics workflow for microbial eukaryotes benchmarked on Giardia intestinalis cells
- 2020
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In: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 21:1
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Journal article (peer-reviewed)abstract
- BackgroundMost diversity in the eukaryotic tree of life is represented by microbial eukaryotes, which is a polyphyletic group also referred to as protists. Among the protists, currently sequenced genomes and transcriptomes give a biased view of the actual diversity. This biased view is partly caused by the scientific community, which has prioritized certain microbes of biomedical and agricultural importance. Additionally, some protists remain difficult to maintain in cultures, which further influences what has been studied. It is now possible to bypass the time-consuming process of cultivation and directly analyze the gene content of single protist cells. Single-cell genomics was used in the first experiments where individual protists cells were genomically explored. Unfortunately, single-cell genomics for protists is often associated with low genome recovery and the assembly process can be complicated because of repetitive intergenic regions. Sequencing repetitive sequences can be avoided if single-cell transcriptomics is used, which only targets the part of the genome that is transcribed.ResultsIn this study we test different modifications of Smart-seq2, a single-cell RNA sequencing protocol originally developed for mammalian cells, to establish a robust and more cost-efficient workflow for protists. The diplomonad Giardia intestinalis was used in all experiments and the available genome for this species allowed us to benchmark our results. We could observe increased transcript recovery when freeze-thaw cycles were added as an extra step to the Smart-seq2 protocol. Further we reduced the reaction volume and purified the amplified cDNA with alternative beads to test different cost-reducing changes of Smart-seq2. Neither improved the procedure, and reducing the volumes by half led to significantly fewer genes detected. We also added a 5′ biotin modification to our primers and reduced the concentration of oligo-dT, to potentially reduce generation of artifacts. Except adding freeze-thaw cycles and reducing the volume, no other modifications lead to a significant change in gene detection. Therefore, we suggest adding freeze-thaw cycles to Smart-seq2 when working with protists and further consider our other modification described to improve cost and time-efficiency.ConclusionsThe presented single-cell RNA sequencing workflow represents an efficient method to explore the diversity and cell biology of individual protist cells.
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| 3. |
- Tice, Alexander K., et al.
(author)
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PhyloFisher : A phylogenomic package for resolving eukaryotic relationships
- 2021
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In: PLoS biology. - : Public Library of Science (PLoS). - 1544-9173 .- 1545-7885. ; 19:8
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Journal article (peer-reviewed)abstract
- Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher (https://github.com/TheBrownLab/PhyloFisher), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic "single-copy orthogroup" datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.
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