Phylogenetic Inference of Homologous/Orthologous Genes among Distantly Related Plants

Equipment

1. Server with a 64-bit Linux-based operating system (Ubuntu 18.04.6 LTS): 512 GB RAM and Intel Xeon (R) Gold 6238 CPU

2. Desktop with a Windows 10 operating system: Intel Core i5-8300H CPU and 8 GB RAM

Software and datasets

Software and databases used in this protocol are as follows:

1. Miniconda3-py39_4.12.0-Linux-x86_64 (https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh)

2. TBtools v1.120 (Chen et al., 2020)

3. Diamond v2.1.7.161 (Buchfink et al., 2015)

4. MAFFT v7.453 (Katoh and Standley, 2013)

5. trimAL v1.4.rev15 (Capella-Gutiérrez et al., 2009)

6. IQ-TREE v2.2.2.6 (Minh et al., 2020)

7. InterProScan 5.63-95.0 (Jones et al., 2014)

8. 1KP dataset (One Thousand Plant Transcriptomes Initiative, 2019)

9. MEME 5.5.3 (Bailey and Elkan, 1994)

10. iTOL (Interactive Tree Of Life) (Letunic and Bork, 2021)

11. Jalview v2.11.2.0 (Waterhouse et al., 2009)

Procedure

We show a detailed procedure for homologs/orthologs identification with large-scale genomic data (Figure 1).

Figure 1. Pipeline for homologs/orthologs identification with large-scale genomes and transcriptomes

1. Software installation

   1. Miniconda3-py39_4.12.0-Linux-x86_64

      Miniconda3 is a package manager for downloading and installing bioinformatics software. It can be downloaded and installed in the server by the following commands:

      
      

      wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh

      bash Miniconda3-py39_4.12.0-Linux-x86_64.sh

   
   

   2. Diamond v2.1.7.161

      Diamond is a fast protein aligner for protein sequences that can be downloaded and installed using the following commands:

      
      

      wget https://github.com/bbuchfink/diamond/releases/download/v2.1.7/diamond-linux64.tar.gz

      tar -zxvf diamond-linux64.tar.gz

      
      

      Add the path of Diamond to the environment variable.

   
   

   3. MAFFT v7.453

      MAFFT is a package for sequence alignment that can be installed by conda:

      
      

      conda install mafft

   
   

   4. trimAL v1.4.rev15

      TrimAL is a tool for automated alignment trimming.

      
      

      wget https://github.com/inab/trimal/archive/refs/tags/v1.4.1.tar.gzhttps://github.com/inab/trimal/archive/refs/tags/v1.4.1.tar.gz

      tar -zxvf trimal-1.4.1.tar.gz

      cd ./trimal-1.4.1/source/

      make

      
      

      Add the current directory to the environment variable after compilation.

   
   

   5. IQ-TREE v2.2.2.6

      Q-TREE 2 is a widely used tool for maximum-likelihood phylogeny inference.

      
      

      wget https://github.com/iqtree/iqtree2/releases/download/v2.2.2.6/iqtree-2.2.2.6-Linux.tar.gz

      tar -zxvf iqtree-2.2.2.6-Linux.tar.gz

      
      

      Add the ./iqtree-2.2.2.6-Linux/bin to the environment variable.

   
   

   6. InterProScan 5.63-95.0

      InterProScan is a protein function annotation software. It can be download and installed following the instruction: InterProScan documentation-interproscan-docs documentation (https://interproscan-docs.readthedocs.io/en/latest/).

   7. TBtools v1.120

      TBtools is an integrated tool for bioinformatic analysis and may be downloaded from https://github.com/CJ-Chen/TBtools/releases/download/1.123/TBtools_windows-x64_1_123.exe.

   8. Jalview v2.11.2.0

      Jalview a free cross-platform program for multiple sequence alignment editing, visualization, and analysis. This software may be downloaded from https://www.jalview.org/.




2. Genome and transcriptome download and processing

   1. Plant genomes download.

      A total of 39 streptophytes (land plants and charophytes), 54 chlorophytes, 9 rhodophytes, and 1 glaucophyte were selected, covering all main clades of Archaeplastida (Table S1). The protein sequences or coding sequences (CDS) and GFF annotation files were downloaded. The used transcriptomes data of algae are available at the 1KP website (https://db.cngb.org/onekp/).

   2. Removing redundant transcripts and short genes.

      Based on the GFF annotation files of each genome, the redundant transcripts and short genes are removed by TBtools: (a) the longest transcript of each gene is retained to remove redundancy resulting from alternative splicing variations (detailed steps are briefly displayed in Video 1); (b) protein sequences length of genes is calculated by TBtools, and sequences shorter than 50 amino acids are manually filtered.

      
      
      
      Video 1. Removing the redundant transcripts by TBtools

      
      

3. Identifying orthologous genes within large-scale genomic/transcriptomic data

   1. Identifying candidate homologs from genomes.

      
      

      In order to identify candidate homologs of Arabidopsis thaliana HLS1 (AtHLS1), we conducted similarity searches with relative stringency threshold (E value < 1 × 10^-5) against the protein sequences of plant genomes. For genomic data, the relative stringency threshold (E value < 1 × 10^-5) is used, and the reasons include: (1) gene annotation of genomic data is based on multiple evidences (such as gene database and transcriptomic data), which ensured the completeness and quality of gene sequences; (2) the genomic data in this study is mainly derived from the Phytozome (https://phytozome-next.jgi.doe.gov/) and has high qualities. We provide brief descriptions of files used in the command line in Table S2.

      
      

      diamond makedb --in all_genome_sequences.fa -d all_genome_sequences

      diamond blastp --db all_genome_sequences.dmnd --query AtHLS1.fa --out genome_out.result --outfmt 6 --sensitive -e 1e-5 --block-size 1.0 --index-chunks 1

   
   

   2. Identifying candidate homologs from transcriptomes.

      We used a relatively relaxed threshold to search candidate homologs from plant transcriptomes (E value < 1 × 10^-2). The reason includes the intrinsic incompleteness of transcriptomes resulting from alternative splicing and premature termination. The detailed steps are briefly displayed in Video 2.

      
      
      
      Video 2. BLASTp for AtHLS1 at the 1KP website

      
      

   3. Filtering sequences with blast-hits.

      Candidate homologs from both genomes and transcriptomes are integrated into a single fasta file (ID_sequences.fa), and InterProScan database is used to filter homologous sequences without a conserved functional domain: N-acetyltransferase (PF00583). Consolidate the homologous sequences with “N-acetyltransferase” domain into a new fasta file (PF00583_ID_sequences.fa) for further phylogenetic analyses.

      
      

      Interproscan.sh -i ID_sequences.fa -f tsv -appl Pfam -o interproscan_ID_sequences.txt




4. Orthologs inference with phylogenetic analyses

   1. Alignment and trimming of homologous sequences.

      Homologous sequences are aligned by MAFFT using the following command:

      
      

      mafft PF00583_ID_sequences.fa > mafft_out.fa

      
      

      Note: An alignment of up to ~200 sequences × ~2,000 sites is suitable for an accurate option (L-INS-i), and an alignment of <~30,000 sequences is suitable for fast option (FFT-NS-2).

      
      

      TrimAl is a widely used tool for automated alignment trimming in large-scale phylogenetic analyses. Before trimming, we perform manual inspection for sequence alignment using Jalview and exclude one sequence (in red rectangle) for its error alignment (Figure 2). We use a relatively relaxed threshold (‘-gt=0.13’: retaining columns that have at least 13% gap-free sites for keeping as much informative sites of conserved domains as possible) for trimming multiple sequence alignment (MSA). The trimmed MSA is further visually inspected to (1) filter the obvious ambiguously aligned regions and (2) retain the regions of functional domains, ensuring a greater proportion of reliably phylogenetically informative sites.

      
      

      trimal -in mafft_out.fa -out mafftout_0.13.fas -gt 0.13

      
      

      
      Figure 2. Multiple sequence alignment of AtHLS1 and its homologs. We empirically focused on the alignment region of conserved domains and found an obvious sequence that is poorly aligned (no amino acid residue was aligned).

   
   

   2. Constructing a phylogenetic tree of homologous proteins.

      The maximum likelihood phylogenetic tree of homologous proteins is inferred using IQ-TREE 2, and the best-fitting model is determined by ModelFinder. Branch supports are evaluated by the ultrafast bootstrap (UFBoot) approach and SH approximate likelihood ratio test (SH-aLRT test) with 1,000 replicates.

      
      

      iqtree2 -s mafftout_0.13.fas -st AA -m MF

      iqtree2 -s mafftout_0.13.fas -m Q.plant+I+R6 -alrt 1000 -bb 1000 –bnni -pre 0.13_result

   
   

   3. Confirmation of orthologous sequences using function domains/motifs.

      The conserved functional domains/motifs are analyzed by MEME suite. The parameter “number of motifs expected to be found” is set to ten (a common setting for this parameter) and other parameters are as default. Corresponding functional domains and motifs of each orthologous sequence are mapped to the phylogenetic tree using iTOL (Video 3). Two conserved residues (L327 and E346 in AtHLS1) are checked and highlighted in the MSA. Both the conserved functional domains/motifs and residues are used for the confirmation of orthologous sequence (Figure 3).

      
      
      
      Video 3. Mapping the domain/motifs to the phylogenetic tree

      
      

      
      Figure 3. Phylogenetic tree, two conserved residues, and conserved motifs of plant HLS1 homologs

Acknowledgments

This work is supported by the Natural Science Fund for Colleges and Universities in Jiangsu Province of China (21KJB180015). This protocol was adapted from our recent work (Wang et al., 2023). We thank the editor and anonymous reviewers for their helpful suggestions.

Competing interests

The authors declare that there are no conflicts of interest or competing interests.

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Supplementary information

The following supporting information can be downloaded here:

1. Table S1. Sources of the genomic data used in this protocol
2. Table S2. Brief description of files used in the command line