Biodiversity Data Journal 11: e98143 OO)
doi: 10.3897/BDJ.11.e98143 open access
Data Paper

Comparison of gut bacterial communities of
Hyphantria cunea Drury (Lepidoptera, Arctiidae),
based on 16S rRNA full-length sequencing

Hui Gao*S, Sai Jiang+, Yinan Wang*?, Meng Hul, Yuyan Xue", Bing Cao*, Hailong Dout, Ran Lit,
Xianfeng Yit, Lina Jiang*, Bin Zhang”, Yujian Lit

+ School of Life Sciences, Qufu Normal University, Qufu, China

§ School of Life Sciences, Shandong University, Qingdao, China

| Forestry Protection and Development Service Center of Jining City, Jining, China

| Qufu Bureau of Natural Resources and Planning, Qufu, China

# Animal Husbandry and Fisheries Development Centre of Tengzhou, Tengzhou, China

= College of Life Sciences and Technology, Inner Mongolia Normal University, Hohhot, Inner Mongolia
Autonomous Region, China

Corresponding author: Yujian Li (yujian528@163.com)
Academic editor: Anna Sandionigi
Received: 29 Nov 2022 | Accepted: 14 Apr 2023 | Published: 10 May 2023

Citation: Gao H, Jiang S, Wang Y, Hu M, Xue Y, Cao B, Dou H, Li R, Yi X, Jiang L, Zhang B, Li Y (2023)
Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, Arctiidae), based on 16S
rRNA full-length sequencing. Biodiversity Data Journal 11: €98143. https://doi.org/10.3897/BDJ.11.e98143

Abstract

There are a large number of microorganisms in the gut of insects, which form a symbiotic
relationship with the host during the long-term co-evolution process and have a significant
impact on the host's nutrition, physiology, development, immunity, stress tolerance and
other aspects. However, the composition of the gut microbes of Hyphantria cunea remains
unclear. In order to investigate the difference and diversity of intestinal microbiota of H.
cunea larvae feeding on different host plants, we used PacBio sequencing technology for
the first time to sequence the 16S rRNA full-length gene of the intestinal microbiota of H.
cunea. The species classification, B diversity and function of intestinal microflora of the 5"
instar larvae of four species of H. cunea feeding on apricot, plum, redbud and Chinese ash
were analysed. The results showed that a total of nine phyla and 65 genera were identified
by PacBio sequencing, amongst which Firmicutes was the dominant phylum and
Enterococcus was the dominant genus, with an average relative abundance of 59.29% and

© Gao H et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY
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credited.

2 Gao H et al

52.16%, respectively. PERMANOVA analysis and cluster heat map showed that the
intestinal microbiomes of H. cunea larvae, fed on different hosts, were significantly
different. LEfSe analysis confirmed the effect of host diet on intestinal community structure
and PICRUSt2 analysis showed that most of the predictive functions were closely related
to material transport and synthetic, metabolic and cellular processes. The results of this
study laid a foundation for revealing the interaction between the intestinal microorganisms
of H. cunea and its hosts and provided ideas for exploring new green prevention and
control strategies of H. cunea.

Keywords

Hyphantria cunea, intestinal microbiome, 16S rRNA full-length gene, host diet, full-length
sequencing

Introduction

The gut of an insect is home to a vast and diverse microbial community. In the process of
long-term co-evolution with the host, intestinal microbes have also formed extremely
diverse population structures and biological functions, playing an important role for insect
survival (Ayyasamy et al. 2021), development (Elgart et al. 2016), reproduction, mating
(Wei et al. 2021), immunity (Yao et al. 2018), metabolism (Ceja-Navarro et al. 2015), cold
tolerance (Mueller et al. 2011), heat resistance (Dunbar et al. 2007), degradation of
exogenous toxins (Chen et al. 2020, Jiang et al. 2021), antioxidant (Jiang et al. 2021),
adaptation to the environment (Elgart et al. 2016, Ayyasamy et al. 2021), hormone signal
transduction (Zeng et al. 2020) and absorption of nutrients (Vang et al. 2016) etc.

Hyphantria cunea Drury (Lepidoptera, Arctiidae) is a plant quarantine pest, native to North
America (Bai et al. 2020). It has caused great harm to agriculture and forestry because of
its strong adaptability, fast propagation speed and wide host range. H. cunea mainly feeds
on plant leaves with larvae and causes harm. In serious cases, it can eat all the leaves of
the host plant and eat the bark, thus weakening the ability of trees to resist damage and
stress and seriously affecting the survival and growth of trees. H. cunea has a wide and
varied diet; studies have shown that the selection of the insect is regulated by the
chemosensory system (Obiero et al. 2014) and a large expansion of the chemosensory
gene family of H. cunea makes it have multiple feeding habits (Chen et al. 2020). At the
same time, the enhanced utilisation capacity of new nutrient sources also promotes the
rapid adaptation and diffusion of this species in its whole range (Wu et al. 2019), thus
causing great damage to agriculture, forestry and ecological environment.

At present, there are many prevention and control measures for H. cunea, such as
quarantine, webs clearance, light trapping, chemical control, microbial insecticide (VVang et
al. 2021), multi-species trapping (Brockerhoff et al. 2013), research and development of
transgenic insect-resistant plants (Yang et al. 2016), RNA interference (Wang et al. 2019)
etc. However, large-scale spraying of pesticides will kill the natural enemies of the H.

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 3

cunea, increasing the resistance of H. cunea itself and destroying the ecological
environment. As the natural enemy of H. cunea, Chouioia cunea Yang has the advantages
of environmental friendliness, simple and convenient artificial mass breeding technology,
easy to operate etc. Dong conducted bee release experiments in Neihuang County and
achieved good results (Dong 2021). However, biological control also has disadvantages,
such as higher cost and the control effect is susceptible to environmental conditions
(Zheng et al. 2012). Some scholars predicted that the potential distribution of H. cunea in
the Northern Hemisphere (North America, Europe and Asia) is expected to increase
gradually over time (Ge et al. 2019). The prevention and control situation of H. cunea is still
serious, so it is urgent to explore new control methods of H. cunea.

With the development of high-throughput sequencing technology and omics technology, a
growing number of researchers are studying insect gut microbes (Cao and Ning 2018). For
example, Lactobacillus plantarum can not only affect the mating preference of Drosophila
melanogaster, but also affect the mutual recognition before mating by prolonging the
mating latency (Wei et al. 2021). The gut microbiota of two Lepidopteran herbivores
Dendrolimus superans and Lymantria dispar may become altered under plant defensive
compound-induced stresses (Zeng et al. 2020). The antioxidant and detoxification defence
systems, as well as the gut microbiota of H. cunea larvae, constitute an effective counter-
defence regulatory mechanism against the secondary metabolites, based phytochemical
defence (Jiang et al. 2021). In the presence of exogenous toxins, cadmium and selenium,
honeybee gut microbes can not only remove toxins from the gut, but also respond to
challenges by upregulation of metabolites related to detoxification (Rothman et al. 2019).

There ismore and more research on the gut microbiota of insects, but the existing research
on the intestinal microbiota of H. cunea only stays in the next-generation sequencing
stage. Compared to the V3-V4 region of the 16S rRNA gene amplicon, full-length
sequencing has higher resolution and fewer inaccurate sequences (Burke and Darling
2016). At the same time, this technology has an advantage of longer reads which can
easily sequence complete 16S rRNA gene or other marker genes. This advantage can help
scientists carry out a phylogenetic microbial community profiling with higher resolution
(Yang et al. 2021). Studies have shown that full-length sequencing can be used to detect
rarer species (Deutscher et al. 2018) and the accuracy of full-length sequencing annotation
was Significantly improved compared with that of the short V3-V4 region sequencing
(Wang et al. 2021). The V4 region of 16S rRNA gene sequencing has the advantage of
detecting archaea. However, this group often occupies low abundance and has seldom
been suggested to play important roles in insect hosts (Yang et al. 2021). With the
declining price of third-generation sequencing, we prefer to recommend using this
technology to sequence full-length of 16S rRNA gene in insect microbiome research. In this
study, the full-length 16S rRNA of intestinal microbiota was detected by PacBio third-
generation sequencing technology in order to clarify the diversity and unity of intestinal
microbiota in larvae of H. cunea with different diets, thus providing new ideas for the
control of H. cunea.

4 Gao H et al

Methods
Insect rearing and sample processing

In this study, larvae of H. cunea were selected as experimental materials, which fed on four
host plants: apricot (A), plum (P), redbud (R) and Chinese ash (CA). The experimental
materials were collected in September 2021 on the campus of Qufu Normal University and
reared with fresh leaves in an indoor artificial climate chamber at 70 + 5% RH and 25 + 1°C
under a 14:10 L: D photoperiod (Jiang et al. 2021). The 5" instar larvae were selected as
experimental insects.

The 5" instar larvae of H. cunea were dissected on a super clean workbench. After
sterilisation with an autoclave steriliser, the anatomical tools were irradiated with an
ultraviolet lamp for 30min. The body surface of the larva was disinfected with 75% alcohol
for 90 s and then cleaned with sterile water for 3 times. The complete intestine was
removed and placed into a sterilised 2 ml centrifuge tube and stored at -80°C. Every 10
larva intestines were taken as one sample, each treatment had three replicates.

DNA extraction

The DNA was extracted with the TGuide S96 Magnetic Soil /Stool DNA Kit (Tiangen
Biotech (Beijing) Co., Ltd.) according to the manufacturer's instructions. The DNA
concentration of the samples was measured with the Qubit dsDNA HS Assay Kit and Qubit
4.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, Oregon, USA).

PCR amplification and PacBio sequencing of bacterial 16S rRNA gene

The 27F: AGRGTTTGATYNTGGCTCAG and 1492R: TASGGHTACCTTGTTASGACTT
universal primer set was used to amplify the full-length 16S rRNA gene from the genomic
DNA, extracted from each sample. Both the forward and reverse 16S rRNA gene primers,
were tailed with sample-specific PacBio barcode sequences to allow for multiplexed
sequencing. We chose to use barcoded primers because this reduces chimera formation
as compared to the alternative protocol in which primers are added in a second PCR
reaction. The KOO One PCR Master Mix (TOYOBOLife Science) was used to perform 25
cycles of PCR amplification, with initial denaturation at 95°C for 2 min, followed by 25
cycles of denaturation at 98°C for 10 s, annealing at 55°C for 30 s and extension at 72°C
for 1 min 30 s, with a final step at 72°C for 2 min. The total of PCR amplicons were purified
with Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN) and quantified
using the Qubit dsDNA HS Assay Kit and Qubit 4.0 Fluorometer (Invitrogen, Thermo Fisher
Scientific, Oregon, USA). After the individual quantification step, amplicons were pooled in
equal amounts. SMRThell libraries were prepared from the amplified DNA by SMRTbell
Express Template Prep Kit 2.0 according to the manufacturer's instructions (Pacific
Biosciences). Purified SMRTbell libraries from the pooled and barcoded samples were
sequenced on a single PacBio Sequel II 8M cell using the Sequel Il Sequencing kit 2.0.

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 5

Shenzhen Wekemo Technology Group Co., Ltd. was entrusted to build the database and
we used PacBio platform for sequencing (Caporaso 2010, Bokulich 2013, Jovel 2016).

Statistical and bioinformatics analysis

After the base-calling analysis, the original data files were transformed into FASTQ format.
QIIME2 (Quantitative Insights Into Microbial Ecology) package was used for quality control,
denoising, stitching and chimerism removal of all original sequences of all samples to
cluster tags at a similarity level of 99% and to obtain OTU (operational taxonomic units)
(Edgar 2013). Sequences identified as chloroplast, mitochondrial sequences and
sequences that did not align to bacteria were removed from the dataset. The taxonomic
annotation of OTU was performed, based on the Silva (https:/www.arb-silva.de) database.
We used Analysis of Variance (ANOVA) to assess differences in community richness
amongst different feeding groups. We used PCOA analysis and NMDS analysis, based on
Bray—Curtis dissimilarity to analyse the bacterial community data. Stress values were used
to assess the quality of NMDS representation; stress values < 0.2 are considered a good
representation of the data, while those > 0.3 are considered invalid (Quinn and Keough
2002). Linear Discriminant Analysis (LDA) was used to screen the biomarkers for statistical
differences between different groups with LDA scores greater than 4. We used PICRUSt to
predict the composition of known gut microbial gene functions (Langille et al. 2013).

Results
Analysis of 16S rRNA sequencing results

A total of 301,266 original reads were obtained from 12 samples of H. cunea larvae with
redundancy removed. The lowest number of sample sequence was 11,019. After quality
control, 271,869 reads were retained from the original 301,266 reads (Table 1). Cluster
analysis revealed 362 OTUs, including nine phyla, 69 genera and 103 species. With the
increase of sequence depth, both Shannon Index rarefaction curves (Fig. 1a) and sample
rarefaction curves (Fig. 1b) did not increase significantly, indicating that the sequencing
volume was sufficient.

Table 1.

Summary statistics of 16S rRNA sequences from 12 samples of Hyphantria cunea*".

Sample ID Raw Reads Filtered Denoised Non-Chimeric Effective (%)
R1 24,754 24,442 23,712 23,712 95.79
R2 11,019 10,857 10,602 10,602 96.22
R3 13,574 13,421 12,989 12,989 95.69
Al 15,107 14,929 14,592 14,592 96.59
A2 25,380 25,054 24,585 24,285 95.69

A3 34,567 34,087 33,534 33,457 96.79

6 Gao H et al

Sample ID Raw Reads Filtered Denoised Non-Chimeric Effective (%)
P1 39,238 38,640 37,979 30,046 76.57
P2 35,837 35,219 34,443 31,545 88.02
P3 39,724 39,186 38,466 35,601 89.62
CA1 16,477 16,291 16,023 14,725 89.37
CA2 28 634 28,205 27,608 24,946 87.12
CA3 16,955 16,730 16,163 15,369 90.65

5
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Number of sequences Number of sequences
(a) (b)
Figure 1. EES]

The rarefaction curves show that the sequencing volume is sufficient. (a) Sample rarefaction
curves; (b) Shannon Index rarefaction curves. CA, Chinese ash-feeding; A, apricot-feeding; P,
plum-feeding; R, redbud-feeding.

Comparison of intestinal microbial diversity of Hyphantria cunea fed on
different host plants

The OTUs obtained were compared with the microbial grouping database to obtain the
species classification information corresponding to each OTU. The microbial composition
of each sample was recorded at levels of phylum, class, order, family, genus and species.
We found nine abundant phyla (Fig. 2a), including Firmicutes, Proteobacteria,
Cyanobacteria, Fibrobacterota etc. Firmicutes and Proteobacteria were the dominant
phyla. Firmicutes was the dominant phylum in the apricot feeding group (84.7%) and
redbud feeding group (80.1%), Proteobacteria was the dominant phylum in the Chinese
ash feeding group (54.5%) and plum feeding group (72.1%). The relative abundance of
Cyanobacteria in the apricot feeding group was the highest (14.26%). In addition, some
special phyla existed only in individual feeding groups, for example, a small number of
Bdellovibrionota was annotated in the plum feeding group. At the genus level, a total of 65
genera were annotated and the top 20 genera with the highest abundance were selected

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 7

to draw a histogram (Fig. 2b). Amongst them, Enterococcus was the absolute dominant
genus. The relative abundance of Enterococcus annotated in the redbud feeding group
was the highest (72.1%), followed by the apricot feeding group (67.2%) and the relative
abundance of plum feeding group was the lowest (24.9%). Enterobacter was annotated in
all groups, with the highest relative abundance in Chinese ash feeding group (43.26%).
Acinetobacter, Pseudomonas and Staphylococcus were annotated in each feeding group,
the most Acinetobacter and Pseudomonas were annotated in Chinese ash feeding group
(8.06% and 0.51%, respectively) and the most Staphylococcus was annotated in apricot
feeding group (0.38%).

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Bl Bdellovibrionota _ Fibrobacterota Bj Planctomycetota Wlother Limnohabitans Salmonella

__ Actinobacteriota Wicampilobacterota " Bacteroidota _ Glutamicibacter Misrevibacterium | Methylobacterium

Gl Cyanobacteria |) Proteobacteria Wl Firmicutes Bisphingobacterium Coxiella Bj Lactobacillus
Helicobacter MAcinetobacter | Chloroplast

Pseudomonas | Klebsiella Mlunclassified

) Empedobacter [pantoea MlEnterobacter

"Staphylococcus Bivycoplasma ffenterococcus

(a) (b)

Figure 2. EES]

Histogram of intestinal microorganisms of Hyphantria cunea larvae at the level of phylum (a)
and the level of the top 20 genera with the highest abundance (b). Different colours represent
different species, and the height of the colour block indicates the proportion of the species in
relative abundance. Other species are combined as "Other" and unannotated classification
dates are incorporated as "Unknown".

We selected six different species and compared the differences of intestinal microbiota
amongst feeding groups (Fig. 3). The three species of Enterobacter (E. asburiae, E.
cloacae and E. hormaecheil) had the highest relative abundance in the Chinese ash
feeding group (CA). The relative abundance of E. casseliflavus and G. haemolysans in the

8 Gao H et al

Chinese ash feeding group (CA) were higher than that in other feeding groups and there
were significant differences between Chinese ash feeding group (CA) and other feeding
groups, while there was no significant difference amongst the remaining three feeding
groups. The relative abundance of Enterococcus sp. contained in the plum feeding group
(P) was the highest, followed by the apricot feeding group (A). The relative abundance of
Enterococcus sp. in the Chinese ash feeding group (CA) and the redbud feeding group (R)
was the lowest and there was no significant difference between these two groups.

Relative abundance

Figure 3. EESl

Multiple comparison histogram of intestinal microbiome at the species level. Different
lowercase letters on columns indicate significant differences (ANOVA and Duncan test, P <
0.05) in the mean values.

The OTUs of intestinal microflora of Hyphantria cunea larvae in four feeding groups:
Chinese ash (CA) feeding group, plum feeding group (P), redbud feeding group (R) and
apricot feeding group (A) were compared and 87, 73, 69 and 62 OTUs were annotated,
respectively (Fig. 4). Amongst them, the number of OTUs annotated in the Chinese ash
feeding group (CA) was the highest and the number of OTUs annotated in the apricot
feeding group (A) was the lowest. Only seven OTUs were shared by four groups and the
number of OTUs shared between each two groups was small, indicating that there were
differences in intestinal microbial communities amongst different feeding groups of
Hyphantria cunea.

In order to reveal the dynamic changes of gut microbes, we choose the 20 most abundant
genera to draw a cluster heat map. Based on the similarity of species abundance, the

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... ss)

cluster heat map can reflect the data information in the two-dimensional matrix through
colour change and similarity degree (Fig. 5). Vertical clustering refers to sample information
and horizontal clustering refers to species information. The relative abundance of
Brevibacterium and Staphylococcus were the highest in the apricot feeding group (A). In
the plum feeding group (P), the highest relative abundance was found in Pantoea, followed
by Lactobacillus. The relative abundances of Methylobacterium and Coxiella in the redbud
feeding group (R) were the highest. Enterobacter, Thiothrix, Acinetobacter and
Sphingobacterium had the highest relative abundances in Chinese ash feeding group (CA).

P R

Figure 4. EES]

Venn diagram of OTUs from unique species owned by each sample and common species
shared by two or more samples.

In this PCoA scatter plot, the horizontal and vertical coordinates represent the two
characteristic values that have the greatest influence on the difference between samples,
with the influence degree of 28.85% and 22.05%, respectively (Fig. 6a). PERMANOVA
analysis showed that there was no significant difference between the redbud feeding group
(R) and the apricot feeding group (A) (PERMANOVA: F = 1.453, P = 0.4), but there were
significant differences between the other feeding groups (PERMANOVA: F = 2.928, P=
0.001). NMDS analysis based on the Bray—Curtis distance also confirmed this result (Fig. 6
b PERMANOVA: F = 2.928, P= 0.001 metaMDS: stress = 0.127).

10 Gao H et al

Pantoea ' zie
Lactobacillus 1
Glutamicibacter 0.5
Enterobacter 0
Sphingobacterium ‘ -0.5
Acinetobacter

Thiothrix

Brevibacterium

Salmonella
Limnohabitans
Helicobacter
Empedobacter
Mycoplasma
Klebsiella
Pseudomonas
Methylobacterium

Coxiella

Figure 5. EES]

Cluster heat map of the 20 most abundant genera in the bacterial community. The columns
represent the samples and the rows represent the bacterial OTUs assigned to the genus level.
Dendrograms of hierarchical cluster analysis grouping genera and samples are shown on the
left and at the top, respectively.

To find biomarkers with statistical differences between groups, we used linear discriminant
analysis (LDA) effect size (LEfSe) to screen out different levels of taxa (kingdom, phylum,
class, order, family, genus and species) between groups, based on standard LDA values
greater than four (Fig. 7a). At the same time, we drew the cladogram from phylum to
species to fully understand the distribution of these different taxa at different taxonomic
levels (Fig. 7b). In the plum feeding group (P), the gut microbiota of H. cunea had the most
different taxa (LDA > 4), and there were nine taxa, amongst which Enterobacterales was

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 11

the most highly discriminating taxon. There were significant differences in intestinal
microbiota of the Chinese ash feeding group (CA) amongst the five taxa, which belonged
to Bacteroidetes and Proteobacteria, respectively. The three taxa in apricot feeding group
(A) belonged to Actinobacteria and Firmicutes. Finally, the Enterococcus in the intestinal
microbiota of redbud feeding group (R) belonged to Firmicutes. Taken together, these
analyses confirm the effect of host diet on intestinal community structure, similar to what
has been observed in other Lepidopteran herbivores.

PCo2 (22.05%)
NMDS2

k
‘le

PCo1 (28.85%) NMDS1
(a) (b)

Figure 6. EES]

PCoA and NMDS of the gut microbiota of H. cunea. (a) Principal co-ordinates analysis (PCoA)
with Bray—Curtis dissimilarity of the bacterial community between different host diets (b) Non-
metric multidimensional scaling (NMDS) diagrams of 12 samples, based on the Bray—Curtis
matrix. CA, Chinese ash-feeding; A, apricot-feeding; P, plum-feeding; R, redbud-feeding.

To determine the potential relationships amongst bacteria in the gut microbes of Hyphantria
cunea, we performed a common network analysis, based on sample genus composition
(Fig. 8). Firmicutes and Proteobacteria were the dominant phyla and the relative
abundance of Enterococcus in Firmicutes was the highest.

Most bacteria have positive interactions with each other, that is, the larger the value of one
variable is, the larger the value of another variable will be. We speculate that there is a
synergistic relationship between bacteria groups with a positive interaction relationship.
There were also negative interactions between individual bacteria, that is, the larger the
value of one variable was, the smaller the value of another variable was.There were
negative interactions between Coxiella and Staphylococcus (p = -0.61), between Coxiella
and Methylobacterium (p = -0.51) and between Enterococcus and Acinetobacter (p =
-0.56). We speculated that there was an antagonistic relationship between these bacteria.

Functional prediction of gut microbiota

In order to better understand the important functions of intestinal microorganisms of
Hyphantria cunea, PICRUSt2 software was used to predict the composition of functional
genes in the samples according to the OTU abundance table and OTU sequence and to
draw the Circos graph of intestinal microbial, based on the KEGG L3 pathway (Fig. 9). The

12 Gao H et al

results showed that most of the predicted functions were related to metabolic and cellular
processes, including biosynthesis, amino acid metabolism, protein transport, mismatch
repair etc., representing the most active functions in the intestinal tract.

ee CAga A mm P go R
s__Enterococcus_casselific (a)

vus 0__Enterobacterales
gl ir Sterne sete
p__ Proteobacteria
s_antosa_sp_ ee |
ee
4
g__Pantoea
s__Pantoea_rwandensis es
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C 8a eee SS Sea]
s__Enterococcus_sp_
C__Bacteroicia en
s__Enterobacter_hormacc

hei
p__Bacteroidota :

s__Klebsiella_aerogenes 0 1 2 3 4 5 6

LDA SCORE (log 10)

(a)

a: c__Bacteroidia

: p__Bacteroidota :
c:s__ Enterococcus_casseliflavus
d:s__ Enterococcus_sp_

e:c__ Bacilli

f: p__Firmicutes

9: s__Enterobacter_asburiae _
:s_ Enterobacter_hormaechei
i: s~ Klebsiella_aerogenes

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:g__Kluyvera :

:$ _Pantoea_rwandensis

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35

_Erwiniaceae
._Enterobacterales
Gammaproteobacteria

roteobacteria

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(b)

Figure 7. EES

Abundance of different gut microbial taxa observed in the four groups using linear discriminant
analysis effect size (LEfSe analysis). (a) Bacterial taxa with linear discriminant analysis (LDA)
score greater than four in the gut microbiota of Hyphantria cunea feeding on different host
plants; (b) Cladogram of bacterial biomarkers, from the phylum (innermost ring) to species
(outermost ring) level, with an LDA score > 4. Differential bacterial taxa are marked by
lowercase letters. Each small circle at different taxonomic levels represents a taxon at that
level and the diameter of the circle is proportional to the relative abundance. Different colours
represent different groups and nodes with different colours represent the communities that
play an important role in the group represented by the colour.

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 13

Phylum
@ Actinobacteriota
© Bacteroidota
@ Bdellovibrionota
@ Campilobacterota

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V4

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> Sp «nA 4 , © Cyanobacteria
YZ, i, < B @ Firmicutes
) \ x LD q/ < @ Planctomycetota
YD Le oe @ Proteobacteria

ey

ostanoligenes_group

Figure 8. EES]

Network analysis of interaction amongst 65 intestinal bacteria genera, based on correlation
analysis (Spearman correlation coefficient p > 0.5). The node represented unique genera and
the size of each node is proportional to the relative abundance. A red edge indicates a positive
interaction between two individual nodes, while a blue edge indicates a negative interaction.

As the largest functional prediction category, the proportion of terpenoid and steroid
biosynthesis was the lowest (1.42%) in the Chinese ash feeding group (CA), but higher in
the apricot feeding group (A) (2.80%) and the redbud feeding group (R) (2.65%). The
function prediction bar of the apricot feeding group (A) and plum feeding group (P) was
relatively higher and the proportion of each function was basically the same in each group.
The highest proportion of bacterial chemotaxis (2.52%) and flagella assembly (2.18%) was
found in the plum feeding group (P), the highest proportion of peptidoglycan biosynthesis
was found in the apricot feeding group (A) (1.74%) and the lowest proportion was found in
the plum feeding group (P) (1.2%). The predicted functions with high abundance were
terpenoid and steroidal biosynthesis, D-alanine metabolism, phosphotransferase system
(PTS), valine, leucine and isoleucine biosynthesis, which were closely related to transport,
synthesis, metabolism and cellular processes. These results suggest that the intestinal
microbiota of Hyphantria cunea plays an important role in accelerating nutrient digestion,
transportation and metabolism and signal transduction.

Discussion

As an invasive species and quarantine pest, H. cunea has caused serious impacts on the
ecological environment, agricultural and forestry development and social and economic
development. For the first time, we used PacBio third generation sequencing technology to
sequence the full-length of 16S rRNA gene of the intestinal microorganism of H. cunea and

14 Gao H et al

detected the intestinal microbe community composition, difference and diversity of H.
cunea larvae feeding on apricot, redbud, plum and Chinese ash to better understand the
relationship between intestinal microbial diversity and feeding habits of H. cunea.
According to the experimental results, we can preliminarily conclude that the diversity and
richness of intestinal microbes of H. cunea are affected by feeding habits.

nh
iosynthesis

D-Glutami
‘amine and O-glutamate Metabolism i i ad

Starch and sucrose metabolism | 5

* &

ane ™
i
S|
an 4

Figure 9. EES]

Circos graph of intestinal microbial, based on the KEGG L3 pathway.

The composition of insect gut microbes is influenced by a variety of
factors, with diet being the most critical

Crotti put forward that operation and development of the microbiome can bring important
practical applications for the formulation of management strategies for insect related

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 15

problems. Specifically, insect microbiome can be manipulated to control agricultural pests,
control pathogens transmitted by insects to humans, animals and plants and protect
beneficial insects from disease and pressures (Crotti et al. 2012). The composition of
intestinal microorganisms is affected by many factors, such as host species, dietary
characteristics, health status and living environment. Pyrosequencing of the high-variable
region V1-V3 of 16S rRNA gene in the intestinal microflora of Blattella germanica showed
that the difference in protein content of food and age of insects affected the structure of
intestinal microflora (Perez-Cobas et al. 2015). By using amplicon sequencing technology,
Yuan detected significant changes in intestinal microbial community richness and diversity
when Grapholita molesta was transferred from an artificial diet to different host plants
(Yuan et al. 2021). By analysing the intestinal flora data of healthy and sick Phasmotaenia
lanyuhensis, it was found that the diversity of intestinal flora and the structure of bacterial
community composition changed significantly under different physiological states (Li et al.
2020). In addition, developmental stage also has an important influence on intestinal
microbes (Gao et al. 2019, Gonzalez-Serrano et al. 2020). It has been proved that different
dietary habits lead to differences in intestinal microbes (Colman et al. 2012). The intestinal
microflora of Plutella xylostella was significantly changed after host transfer (Yang et al.
2020). The intestinal microorganisms of four Lepidopteran fruit pests feeding on different
hosts were significantly different (Liu et al. 2020). After eating different prey, the intestinal
microbiota of Badumna longinqua changed differently, indicating that the changes of
intestinal microbiota are driven by diet and different feeding habits can shape different
microbiota (Kennedy et al. 2020).

The gut microbes of H. cunea play important roles in nutrient acquisition,
metabolism and pesticide degradation

At the phylum level, Firmicutes and Proteobacteria were the dominant phyla (Fig. 2a). The
dominant phylum in the apricot feeding group (A) and redbud feeding group (R) was
Firmicutes, while Proteobacteria was the dominant phylum in the plum feeding group (P)
and Chinese ash feeding group (CA). These results are consistent with previous studies on
the intestinal microbial diversity of H. cunea (Chen et al. 2020) and with the intestinal
dominant flora of many other Lepidoptera insects (Mohammed et al. 2018, Bapatla et al.
2018). Members of Proteobacteria, such as Klebsiella, have been shown to degrade
cellulose (Suen et al. 2018). Firmicutes helps insects digest and obtain nutrients from
cellulose, hemicellulose and other polysaccharides (Brown et al. 2012). Clostridium
sticklandii, which belongs to Firmicutes, plays an important role in the degradation of amino
acids (Yang et al. 2020). The abundance of both Acetobacter of Proteobacteria and
Lactobacillus of Firmicutes increased when Apis mellifera were fed a diet rich in sucrose
and these bacteria can metabolise sugars into monosaccharides and then into acetate
(Fonknechten et al. 2010). Proteobacteria could be functionally crucial for insects to adapt
to specific host plants (Taylor et al. 2019). These studies suggest that Proteobacteria and
Firmicutes may play important roles in host degradation of exogenous substances,
metabolism, nutrient digestion and absorption etc.

16 Gao H et al

At the genus level, Enterococcus is the dominant genus (Fig. 2b). The high relative
abundance of Enterococcus was considered to protect the host against pathogens and
non-commensal microbes from outside and increase the tolerance of a toxic diet by
establishing a colonisation resistance effect in mid-gut (Silverman and Paquette 2008,
Vilanova et al. 2016). Enterococcus has been shown to be abundant in Lepidopteran
insects (Liu et al. 2020, Wang et al. 2020) and these results suggest that Enterococcus
may have some conserved functions in Lepidopteran insects. Pseudomonas was
annotated the most in the Chinese ash feeding group (CA) and Pseudomonas was proved
to be able to degrade insecticides and other foreign biological substances (Indiragandhi et
al. 2007), suggesting that the larvae in the Chinese ash feeding group (CA) had strong
resistance to _ insecticides. Acinetobacter has _lignin-degrading activity, while

Staphylococcus and Sphingobacterium have cellulose and/or aromatic degrading ability.
Both Pantoea and Klebsiella belong to the Proteobacteria family Enterobacteriaceae,
which occur widely in the guts of Lepidoptera and other herbivores and are potentially
beneficial, non-pathogenic microbes (Chen et al. 2016). Pseudomonas is known to harbour
several species able to degrade alkaloids and natural latex or rubber (Vilanova et al. 2016).

The intestinal microbial composition of H. cunea feeding on different
hosts was different and there were complex interactions amongst the

different bacteria

PCOoA analysis and linear discriminant analysis (LDA) showed that the intestinal microbial
composition of H. cunea larvae, fed on different hosts, was different (Figs 6, 7). We found
that there was significant differences between the Chinese ash feeding group (CA) and
other groups, which may be related to the fact that Chinese ash and other plants belong to
different families. There are differences in plant morphology, life form, propagation mode,
internal structure and genes amongst plants of different families, which lead to differences
in nutrients contained in leaves. Interestingly, although plum and apricot belong to
Rosaceae, there were significant differences in gut microbes between the two feeding
groups. One possibility is that the nutrients in plum leaves are very different from those in
apricot leaves. Studies have shown that the oriental fruit moth lays more eggs when using
plums as hosts than when using apples and peaches as hosts (Myers et al. 2006). In the
study of Yuan, the larva of the oriental fruit moth feeding on plums had the maximum
limited growth rate (A) (Yuan et al. 2021). Wang investigated the feeding selection of H.
cunea larvae for common garden plants in Sugian area and found that H. cunea had a
strong selection for plums, indicating that plums are a kind of suitable host (Vang et al.
2020). Host selection by larvae of H. cunea may be affected by leaf thickness, leaf wax
content, leaf proline content, soluble sugar and soluble protein in leaves (Vang et al. 2020
). Further studies are needed to reveal the reasons for the differences in gut microbes
amongst feeding groups. Meanwhile, the results of common network analysis on intestinal
microorganisms of H. cunea showed that most of the bacteria had positive interactions, but
there were also negative interactions (Fig. 8). It is limited to use the 16S rRNA gene
sequence to analyse the diversity of intestinal microbial community. In the future, we can
further study the microbial function by analysing the total genes in the intestinal microbiota

Comparison of gut bacterial communities of Hyphantria cunea Drury (Lepidoptera, ... 17

of insects, namely metagenomic analysis, to understand the functional mechanism of
intestinal microbial community and its impact on the host.

The prediction results of intestinal microbial function in the plum feeding
group were significantly different from those in the other feeding groups

PICRUSt2 analysis showed that the intestinal microbial functions of all feeding groups
were similar with minor differences, mainly focusing on biosynthesis, metabolism and
material transport (Fig. 9). Amongst them, the functional prediction results of the plum
feeding group (P) were significantly different from those of other feeding groups, which was
consistent with PCoA analysis results (intestinal microbial community composition of plum
feeding group (P) was significantly different from that of other groups). At the same time,
the development of omics technology and the application of high-throughput sequencing
technology also provide a more effective method to study the molecular mechanism of the
interaction between insect gut microbe and insect host.

Conclusion

Our results confirmed that the gut bacterial structure of H. cunea can be influenced by the
host plant. The dominant phylum was Firmicutes which fed on apricot and redbud and
Proteobacteria which fed on Chinese ash and plum. At the genus level, the content of
Enterococcus in the plum feeding group was much lower than that in other groups and the
content of Enterobacter in Chinese ash feeding group was much higher than that in other
groups. The cluster heat map showed that the relative abundance of bacteria of different
genera in each feeding group was different, indicating that diet influenced the gut
microbiome of H. cunea. However, there were still many limitations in our study. Some
studies have shown that intestinal microbial change is a gradual process (Fonknechten et
al. 2010, Crotti et al. 2012), but our experiment only observed one instar of the life stage of
H. cunea. Future experiments should be designed to observe the changes in gut bacterial
communities in the H. cunea adaptation to different hosts for successive generations and
the important functions of gut bacterial communities in host adaptation should be
elucidated with the methods of multi-omics. While using the new technology, we should not
abandon the traditional methods of bacterial culture. Our study laid a foundation for further
exploring the differences and functions of intestinal microorganisms of H. cunea and
helped to find new targets for the control of H. cunea.

Data Resources

The raw sequence data reported in this paper have been deposited in the Genome
Sequence Archive (Chen et al. 2021) in the National Genomics Data Center (CNCB-NGDC
Members and Partners 2022), China National Center for Bioinformation / Beijing Institute of
Genomics, Chinese Academy of Sciences (GSA: CRA008201) that are publicly accessible

at https://ngdc.cncb.ac.cn/gsa.

18 Gao H et al

Resource 1

Download URL
Browse - BioProject - CNCB-NGDC Browse - GSA - CNCB-NGDC

Resource identifier

CRA008201

Data format

FASTQ

Usage Rights

Acknowledgements

We thank Dr. Zhou Jing (Qufu Normal University, Qufu) for reading the manuscript and
making some suggestions. This project was supported by the National Natural Science
Foundation of China (No. 31800452), Natural Science Foundation of Inner Mongolia
Autonomous Region of China (No. 2020MS03014 ) and the Young Talents Invitation
Program of Shandong Provincial Colleges and Universities (No. 20190601).

Author contributions

LJ, XY and YL designed the study; HG, YW, MH, YX, BC and BZ collected the data; HG,
HD and RL did the analyses; HG, SJ, LJ and YL wrote the first draft of the manuscript; and
all authors contributed intellectually to the manuscript.

Conflicts of interest

The authors have declared that no competing interests exist.

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Endnotes

“1 Raw reads represent the number of original reads sequenced by PacBio. Filter,
denoise and non-chimeric are three steps of processing raw data. Effective (%) is the
percentage of effective reads in raw reads.