TITLE: Using high throughput amplicon sequencing to determine the diet of two generalist stink bugs (Hemiptera) in agricultural and urban landscapes.
AUTHORS: Olivier Berteloot

[DESCRIPTION]
The use of DNA metabarcoding has become an increasingly popular technique to infer
feeding interactions in herbivores and generalist predators and are especially
useful when the organism of interest is polyphagous. Inferring host plant
preference of native and invasive herbivore insects can be helpful in
establishing effective Integrated Pest Management strategies (IPM). Both stink
bugs, Halyomorpha halys, and Pentatoma rufipes, are known
pests that cause severe economic damage in agroecosystems, primarily commercial
fruit orchards. In this study, we performed Molecular Gut Content Analysis
(MGCA) of these two polyphagous herbivore stink bug species using next-generation
amplicon sequencing (NGAS) of the Internal Transcribed Spacer 2 (ITS2) barcode
region. Additionally, a laboratory experiment with a host switch from a mixed
diet to a monotypic diet with H. halys was conducted to determine the
detectability of the original host plants in a time series up to 3 days after
the host switch occurred. In our field samples, we detected 55 unique plant
genera across the two stink bug species. The sampling location significantly
impacts the observed genera in the diet of both stink bug species, while we
observe no significant seasonal differences. Moreover, this study provides
additional support for the efficiency of DNA metabarcoding techniques to infer the
dietary composition of polyphagous herbivores, delivering species-level
resolution of hostplant-herbivore interactions. Lastly, our study provide an
initial framework for more extensive DNA metabarcoding studies further to
unravel the polyphagous diet of these two pentatomid herbivores.

[BEFORE_START]
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[GUIDELINES]
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[STEPS]
SECTION: Sample collection
1. From 2019-2022, H. halys and P. rufipes were collected in commercial organic orchards (apple, pear and mixed crops), private orchards and urban gardens. 25 locations were sampled throughout the northern parts of Belgium. P. rufipes specimens were hand-caught by scanning trees, shrubs and wildflowerstrips in and around plots in the sampling area fo30 min, spending 1 min per tree, shrub or flower plot, host plants were recorded. H. halys were collected using live and sticky traps baited with pheromones (Pherocon Trécé BMSB) and checked twice per week.

SECTION: PCR and Illumina workflow
5. Library preparation

First amplification

performed using a customer specific primer pair, which contained an additional Illumina TruSeq adaptor sequence (see below) on 1-10 ng of DNA extract (total volume 1µl). 

ITS-S2F 5’ GACGTGTGCTCTTCCGATCT 
ITS-u4 5’ ACACGACGCTCTTCCGATCTR

15 pmol of each forward and reverse primer was used in 20 µL volume of 1x MyTaq buffer containing 1.5 units MyTaq DNA polymerase (Bioline GmbH, Luckenwalde, Germany) and 2 µL of BioStabII PCR Enhancer (Sigma-Aldrich Co.). 
 

 


Second amplification
1 µLof each amplicon obtained in the first PCR was used, and these amplicons were separately amplified in a 20 µL reaction volume using standard i7- and i5- sequencing adaptors.

The second amplification was as first amplification but with a modified annealing temperature, i.e. 3 cycles at 50 °C followed by 7 cycles at 58 °C

SECTION: Sequence analysis
9. Data state

Data is not multiplexed
Paired-end 2x300bp
Quality scores are Phred 33V
SECTION: Sample collection
2. Collected specimens were placed into clean 1.5 mL tubes (Eppendorf) and immediately frozen on dry ice to be stored in the lab at -80 °C

SECTION: DNA extraction
4. Total DNA was extracted from the dissected guts using the DNeasy Blood and Tissue Kit (Qiagen), following manufacturers' instructions.
SECTION: Dissection
3. Prior to DNA extraction, leave specimen for 10 s in 1 % bleach solution. After, the alimentary canal was dissected from crop till rectum, flash frozen in liquid nitrogen in a 1.5 mLL tube with 2 stainless steel beads (5mm diameter) and lysed using a TissueLyser II (Qiagen Inc. Valencia CA USA). 

SECTION: PCR and Illumina workflow
6. Pooling and clean up

DNA concentration of amplicons was assessed by agarose gel electrophoresis. 
20 ng of indexed amplicon DNA of each sample was subsequently pooled (up to 96 samples per pool). 

The pooled libraries were purified with one volume of Agencourt AMPure XP beads (Beckman Coulter, Inc., IN, USA) to remove primer dimer and other small mispriming products, followed by an additional purification on MiniElute columns (QIAGEN GmbH, Hilden, Germany). 
SECTION: PCR and Illumina workflow
7. Size selection and sequencing

The size selection was performed by preparative gel electrophoresis on a LMP-Agarose gel.
Sequencing was done on an Illumina MiSeq (Illumina, Inc., CA, USA) using V3 Chemistry (2x300bp). 
SECTION: Sequence analysis
10. Unzip all .gz files in directory
 
Data are in following path (example):

/home//RAW/1_R1.fastq
/home//RAW/1_R2.fastq

absolute filepaths are needed for the metadata file, and importing in QIIME2
SECTION: Sequence analysis
8. Create environment

Hardware environment specs:
Processor: AMD Ryzen 5 5600X, 3,7 GHz (4,6 GHz Turbo Boost) - 6-Cores - 12 Threads
RAM: Corsair Vengance LPX 128GB (4 x 32GB) DDR4 DRAM 3200MHz C16 Memory Kit
Windows 11 Pro, WSL2, Ubuntu Linux 20.04


Unlock processor in BIOS: press del on start up > advanced settings > virtualization > enable 
Win key > type: CMD > wsl --install
Download Ubuntu in windows app store & install
Double click Ubuntu to start Ubuntu

5. Install Miniconda
 
6. Initialize Miniconda
 
7. Update minidconda
 
8. Install wget
 
9. Install Qiime2 (install the shotgun too, it has the rescript pre-installed)
 
10. Activate Qiime2
 
    
SECTION: Sequence analysis
11. Metadatafile
Create a metadatafile with following information for all samples

absolute forward filepath
absolute reverse filepath
label
location
date
season
coordinates
organism
life stage
type (orchard or garden)
landscape (urban or rural)
hostplant or trap

Create the metadatafile in Google Sheets

File>download> Tab separated values .tsv
SECTION: Sequence analysis
12. Import data into QIIME2
 


 

SECTION: Sequence analysis
13. Trim primers & cut adapters
 

 

SECTION: Sequence analysis
14. Quality filtering, denoising, dereplication and clustering (DADA2)

Looking at the quality fastqc files for each sample. 
All forwards look pretty similar to each other, and all reverses look pretty similar to each other, but worse than the forwards, which is common. 

Phred scores of 20 vs 40 means 1 error per 100 bases vs. 1 error per 10000 bases.

The forward scores drop off at 250 bp, we want to maintain a median phred score of 30 (1 error in 1000). Reverse scores drop off around 185 bp. Knowing our ITS2 fragment is around 350-400 base base pairs and we need some overlap for the reverse and forward reads to be merged, in DADA2 this minimum overlap is 12, more is welcome. We will leave a length of 250p forward and 185bp reverse.
 

 

SECTION: Sequence analysis
15. Local ITS2 BLAST database development

We want to identify the ASV's (rep-seqs.qza), their frequency, and occurence is linked to the frequency table through an ID. But first we must make a local BLASTn ITS2 database of plants 
SECTION: Sequence analysis
15.1. Retrieve available plant ITS2 sequences via NCBI Entrez text query:
 
The resulting records were then downloaded via the “Send to” menu :

send to > Complete record > File > Format: Fasta
SECTION: Sequence analysis
15.2. Rename file and check whether all records were retrieved
 

 

SECTION: Sequence analysis
15.3. Remove linebreaks

(NCBI nucleotide sequences downloaded in FASTA format display linebreaks every 70 bases, this may hinder further sequence processing steps)
 

SECTION: Sequence analysis
18. Assign taxonomy with BLAST, VSEARCH and the Naive Bayes Classifier

 

 

 

SECTION: Sequence analysis
15.4. Collect taxonomic identifiers (taxids) of the organisms from ITS2 sequences were obtained

1. create table linking every accession number to the corresponding nucleotide sequence
 

2. nucl_gb.accession2taxid NCBI file from the FTP website (large file)
 
3. Retrieve the lines with accession numbers and write into temporary file
 
quick check if we have retrieved all of them
 

 

4. Create a table linking accession numbers and taxids
 
    
SECTION: Sequence analysis
15.5. Retrieve a list of unique taxids

 

 

 
SECTION: Sequence analysis
15.6. Collect taxonomic lineages for the taxids

1. The “new_taxdump.tar.gz” NCBI reference file must be downloaded from the NCBI FTP website
 
2. Reformat the rankedlineage.dmp file with simpler field separator (pipe)
 
3. Sort the taxids in rankedlineage.dmp file
 
4. Associate the taxids with their corresponding taxonomic lineages
 

 

5. check for empty lines in the second column in the linking table
 

 

SECTION: Sequence analysis
15.7. Create a table gathering accession numbers, taxids, taxonomic lineages and ITS2 sequences

1. Link accession number to its corresponding taxonomic lineage, by joining both tables and generate a re-ordered 3 column .tsv file
 

 

 


 

SECTION: Sequence analysis
15.8. Create a QIIME2-formatted FASTA file and taxonomic lineage files and import into QIIME2

1. FASTA file creation
 
2. Taxonomic lineages 
 
import sequences into QIIME2 (activate shotgun distribution of Qiime, this includes Rescript).
 

SECTION: Sequence analysis
15.9. Cull sequences 
Sequences displaying 5 or more degenerated bases or containing a homopolymer sequence of 12 or more nucleotides should be removed. Maximize thread usage by setting p-n-jobs to 12 (processor has 12 threads)
 

SECTION: Sequence analysis
15.10. Remove entries that were culled from taxonomy file
 
import cleaned taxonomy file into QIIME2 again
 

SECTION: Sequence analysis
15.11. Dereplicate sequences in the fasta file and taxonomic lineage file
RESCRIPt can be used to remove redundant sequence data (optional step)
 

SECTION: Sequence analysis
15.12. Filter out suspected fungal sequences

1. Sequence and taxonomy data must again be extracted from the .qza files
 
We will blast these plant ITS2 sequences against fungi databases in order to identify sequences to have a suspected fungal orgin.

2. Download ITS siquences manually from the UNITEwebsite
3. Shorten the description line of the FASTA file to match the BLAST command line requirements
 
4. create a local BLAST database from the UNITE ITS sequences
 
5. BLAST plant ITS2 sequences against UNITE fungi database
 
using 12 threads again as this is our maximum

6. Reformat the BLAST output file and add length data for each plant reference sequence
 
7. Remove sequences showing at least 90% identity with UNITE ITS sequences on at least 95% of their length
 
8. Filter suspected fungal sequences from our plant ITS2 reference sequences
 

SECTION: Sequence analysis
15.13. Filter out suspected misidentified sequences

To identify sequences with a wrong identification, our plant ITS2 reference sequences are analyzed in a cross-validation scheme with data leakage, thus were sets of test and training sequences are strictly identical. This allows comparing expected and predicted taxonomies for each sequence and discarding those for which the expected taxonomy at the family rank is observed only once in the top 5 hits resulting from the BLASTn analysis.

1. Create a table linking accession numbers to taxids from the filtered FASTA file
 
2. Generate a BLAST database
 
3. BLAST the plant ITS2 sequences against themselves
 

SECTION: Sequence analysis
15.14. Process leaked cross-validation results to compare expected to predicted taxonomies

1. Retrieve top 5 hits accession numbers and taxids
 
2. Use these files to keep only the top 5 hits for each reference sequence 
 
3. Number the lines for further data processing

 

4. Generate new linking tables that display the taxonomy info at family rank
 
5. Reformat the table so we can write expected taxonomies to it
 
6. Add expected taxonomies to the table
 

SECTION: Sequence analysis
15.15. Count the number of times the expected family is observed in the top 5 hits
 

SECTION: Sequence analysis
15.16. Remove sequences for which the expected family is observed only once in the taxonomy of the top 5 hits
 

SECTION: Sequence analysis
15.17. Import data into QIIME2
 

SECTION: Sequence analysis
19. Export files from QIIME2 for import in R

OTU counts to a .tsv file
 

OTU sequences to .fasta file
 
Taxonomy file to .tsv file
 
metadata is already in .tsv (see step 11)
SECTION: Sequence analysis
16. Train the classifier using the taxonomic lineages and the fasta file


 

SECTION: Sequence analysis
17. Cluster ASVs into OTUs  with 99% similarity

 

SECTION: Sequence analysis
20. R script to create data file, calculate metrics & visualize data