3. RESULTS
3.1 Sequencing quality and ASV
analysis
In this study, the composition of gut microbiota in Catharsius
molossus under starvation and refeeding conditions was analyzed. A
total of 12 samples were sequenced, yielding a range of 49,222 to 69,752
sequences after removing chimeric sequences. The average number of valid
sequences per sample was 66,620. The hungry group exhibited 1,289 ASVs,
while the refed group displayed 2,847 ASVs. There were 419 common ASVs
between the two groups, with 870 ASVs exclusive to the hungry group and
2,428 ASVs exclusive to the refed group
(Fig. 1A). This indicates shared
bacteria within the two gut microbiota groups while also highlighting
their distinctive microbial communities. The sparse curve analysis
showed a plateauing trend with increasing extracted sequence counts,
indicating that the sequencing data volume and depth were appropriately
set for the samples (Fig. 1B).
3.2 Composition and differential analysis of two gut
microbiota
Through ASV annotation, a total of 26 phyla, 45 classes, 68 orders, 177
families, and 399 genera were identified across the two gut microbiota.
At the phylum level (abundance > 1%), the predominant
phyla in Catharsius molossus under starvation conditions were
Proteobacteria (51.93%), Firmicutes (33.11%), Actinobacteria (5.65%),
Bacteroidetes (5.63%), Others (2.66%), and Chloroflexi (1.05%), with
the highest abundance observed in Proteobacteria (Fig. 2A). In contrast,
the predominant phyla in Catharsius molossus under refed
conditions were Firmicutes (44.88%), Proteobacteria (19.73%),
Bacteroidetes (15.16%), Actinobacteria (4.78%), Synergistetes
(4.15%), Candidatus_Saccharibacteria (3.04%), Planctomycetes
(2.31%), Others (2.18%), No_Rank (1.49%), Acidobacteria (1.21%),
and Chloroflexi (1.08%), with the highest abundance observed in
Firmicutes. This reveals distinct predominant phyla between the two
conditions, with the gut microbiota of refed beetles displaying higher
phylum-level diversity (Fig. 2B).
At the genus level, the predominant genera in Catharsius molossusunder starvation conditions were Unassigned (51.02%), Others (15.42%),Vagococcus (15.11%), No_Rank (8.93%), Dysgonomonas(2.66%), Sphingobacterium (1.71%), Gordonia (1.68%),Acinetobacter (1.26%), and Paracoccus (1.13%), with the
highest classified and abundant genus being Vagococcus . In
contrast, the predominant genera in Catharsius molossus under
refed conditions were Others (25.96%), Romboutsia (13.81%),
No_Rank (12.24%), Unassigned (10.09%), Clostridium_XI(7.67%), Proteiniphilum (6.33%), Cloacibacillus(3.83%), Clostridium_sensu_stricto (3.75%),
Saccharibacteria_genera_incertae_sedis (3.04%), Turicibacter
(2.69%), Lysinibacillus (1.9%), Corynebacterium (1.62%),Luteimonas (1.38%), Hydrogenophaga (1.22%),Ercella (1.22%), Dysgonomonas (1.08%), Serpens(1%), Anaerovorax (1%), with Romboutsia being the
highest classified and abundant genus. This indicates higher genus-level
diversity in the gut microbiota of refed beetles, with different
dominant genera observed between the two conditions
(Fig. 2C).
Based on Metastats analysis, comparisons of phylum-level classifications
between the starvation and refed gut microbiota of Catharsius
molossus revealed significant differences (P < 0.05)
in Hydrogenedentes, Proteobacteria, Synergistetes, Planctomycetes,
Verrucomicrobia, Fusobacteria, and Bacteroidetes (Table 2). At the genus
level, 22 genera exhibited significant differences (P <
0.05) (Table 3).
3.3 Alpha diversity and differential analysis of two gut
microbiota
Alpha diversity analysis was conducted based on the Wilcoxon rank-sum
test to compare the richness and diversity indices of the gut microbiota
between the starvation and refed states of Catharsius molossus .
The Chao1 and ACE indices, representing species richness(M. Yang et al.,
2022), were found to be significantly higher in the refed group than in
the starvation group (P < 0.05), indicating a greater
species diversity in the refed microbiota. Additionally, the Shannon
index, reflecting microbial diversity(Zhang et al., 2022), was also
higher in the refed group, while the Simpson index, indicating lower
microbial diversity, was lower in the refed group (P <
0.05). These results collectively suggest that the gut microbiota of the
refed group exhibited both higher richness and diversity compared to the
starvation group, underscoring significant differences between the two
feeding conditions (Table 4).
3.4 Beta diversity and differential analysis of the two gut
microbiota
Principal Coordinate Analysis (PCoA) was employed to evaluate the
similarity in the structure of gut microbiota between the starved and
refed groups(Song et al., 2021). The PCoA plot visualized the primary
sources of dissimilarity among the samples along the horizontal (Axis 1)
and vertical (Axis 2) axes, which accounted for the most significant
variations. Distinct colors on the PCoA plot represented different
groups, with shorter distances indicating greater similarity and reduced
dissimilarity in microbial structures between paired samples. Employing
unweighted UniFrac distances, the PCoA analysis revealed that Axis 1
contributed to 27.89% of the variance, while Axis 2 contributed to
15.47% (Fig. 3). Importantly, the gut microbiota of Catharsius
molossus exhibited clustering patterns corresponding to their feeding
conditions. Notably, the starvation group displayed more scattered
microbial compositions among samples, whereas the refed group
demonstrated a higher degree of intra-group clustering. These findings
imply that under conditions of starvation, Catharsius molossusmay employ diverse strategies in response to environmental changes,
leading to differences in their gut microbiota configurations.
3.5 Functional prediction using
PICRUSt
Functional gene annotations predicted by PICRUSt provided a
comprehensive insight into the metabolic potential of the gut microbiota
in Catharsius molossus. These annotations were mapped to primary,
secondary, and tertiary pathways within the KEGG database, encompassing
various hierarchical levels of metabolic classification. The primary
pathways fell under five fundamental metabolic categories: metabolism,
genetic information processing, environmental information processing,
cellular processes, and organismal systems. Within these primary
pathways, a total of 27 secondary-level pathways and 175 tertiary
pathways were identified. Remarkably, metabolic pathways were dominant
within the primary metabolic category, accounting for approximately 80%
of the total annotations. Among the secondary pathways, amino acid
metabolism exhibited the highest gene abundance, constituting around
15%. Notably, utilizing the Wilcoxon rank-sum test, 32 tertiary
metabolic pathways displayed significant differences (P< 0.05), highlighting potential alterations in the metabolic
landscape between groups at the granularity of these specific pathway
levels (Fig. 4).