Insects constitute the most taxonomically deverse animal group, and this taxonomically diversity is reflected in the community structure and functional capabilities of the microbes found in their digestive systems (Konstantin and Philipp, 2021). The gut microbiota of insects plays a crucial role in nutrient metabolism, including the metabolism of amino acids, lipids, cellulose digestion, nitrogen fixation, and vitamin synthesis (Shamjana et al., 2024). To defend against various pathogens, insects have developed unique and robust immune systems. A core immune priming enables insects to recognize and remember specific pathogens. This ability enables insects to co-evolve with their gut microbiota, activating immune responses that help protect against infections. The symbiotic relationship between insect gut microbes and their hosts significantly contributes to immune defense mechanisms (Sadd and Schmid-Hempel, 2006; Netea et al., 2011; Mikonranta et al., 2014; Chambers and Schneider, 2012; Gerard et al., 2020; Eleftherianos et al., 2021; Humberto and Jorge, 2022). Moreover, gut microbiota assists insects in adapting to their environments and expanding their ecological niches (Kwong and Moran, 2015). The composition of gut microbiota is influenced by external environmental factors and dietary habits, resulting in structural adaptations that reflect the insects' living conditions and feeding strategies (Wu et al., 2020).

The black soldier fly (BSF), an insect belonging to the genus Stratiomyidae (Diptera), is scientifically known as Hermetia illucens (Sheppard et al., 2002). The growth and development of black soldier flies are influenced by several factors, including temperature, relative humidity, substrate, light intensity, and breeding density (Liu et al., 2015; Dou et al., 2019; Zhou et al., 2020; Logan et al., 2021; Luisa et al., 2022). Research indicates that optimal ambient temperatures of 27-30 degrees Celsius enhance the survival rate of black soldier flies while reducing their development time (Tomberlin et al., 2009). High-altitude regions around the world present harsh climatic conditions, characterized by elevated terrain, limited vegetation, low oxygen levels, cold temperatures, higher UV radiation, and reduced atmospheric pressure. These challenging conditions make it difficult for living organisms to thrive (Tiwari et al., 2024). Given the harsh environmental conditions of low temperature and low oxygen in Qinghai province, which are unsuitable for the survival of black soldier flies (Yu et al., 2023), we hypothesize that gut microbiota may play a crucial role in the high-altitude adaptation of this species (Wu et al., 2020).

In 2011, scientists first reported on the composition of the gut microbiota of the black soldier flies (Jeon et al., 2011). Since then, there has been a growing interest in investigating the influence of gut microbiota on these flies and the mechanisms that govern their interactions. Research has shown that various insect species exhibit similar compositions of gut microbiota (Douglas, 2018). The gut microbiota of various insects is influenced by several factors, including feeding habits, age, and environmental conditions, with the predominant phyla being Firmicutes and Proteobacteria (Colman et al., 2012). Notably, the gut microbiota of the black soldier fly hosts a stable core microbiome consisting of Proteobacteria, Firmicutes, Bacteroides, and Actinobacteria, which remains relatively unchanged despite fluctuations in environmental conditions (Engel and Moran, 2013). Numerous studies have demonstrated that the gut microbiota of black soldier fly larvae (BSFL) can enhance nutritional metabolism, contribute to growth and development, regulate immune responses, and improve resistance to various stressors, among other yet-to-be-defined functions (Beuno et al., 2019; Correa et al., 2019; Rehman et al., 2019; Ao et al., 2020; Gold et al., 2020; Elhag et al., 2022; Pei et al., 2022).

The cultivation of Hermetia illucens, commonly known as the black soldier fly, is an emerging industry in China and is well-established in most low-altitude regions of the country. In recent years, Qinghai Province has successfully implemented black soldier fly farming technology at high altitudes. Previous studies have indicated that gut microbiota play a crucial role in helping host insects adapt to high-altitude environments. However, the specific role of gut microbiota in the high-altitude adaptation of black soldier flies has not been extensively studied. To investigate the differences in gut microbiota between black soldier flies cultivated at low altitudes and those transferred to high-altitude regions for domestication and generational rearing, this study focused on these two populations. It compared the composition and diversity of their gut microbiota and predicted their functional roles. The goal was to identify distinct microbiota associated with the high-altitude domestication process and evaluate their potential contribution to the adaptation of black soldier flies to high altitudes. This research enhances our understanding of how gut microbiota affect the high-altitude adaptation of host insects and provides insights that could promote the cultivation of black soldier flies in high-altitude regions, thereby ensuring the production efficiency of this valuable resource.

1.   Materials and methods

1.1   Samples collection

In our study, we conducted experiments involving four treatments: LL, LH1, LH2, and HH. We selected healthy, active, and comparably sized 5^th instar larvae were selected from each of these groups, generating 10 replicates per treatment (40 samples in total). The samples from groups HH, LH1, and LH2 were sourced from Qinghai Kunjie Environmental Protection Technology Co., Ltd. (located at 101°9', 36°7', 2 735 m) in October 2023, representing high-altitude specimens. In contrast, samples from the LL group were collected from Guilin Weili Biotechnology Co., Ltd. (at 110°7', 25°6', 548 m) in November 2023, serving as the low-altitude reference. The LL group consisted of gut samples labeled LL_1 to LL_10, derived from 5th instar larvae of black soldier flies cultivated long-term in Guangxi. The LH1 group samples, identified as LH1_1 to LH1_10, represented the first generation of 5th instar larvae incubated and bred in Qinghai Kunjie Biotechnology Co., Ltd., originating from eggs purchased from Weili Biotechnology Co., Ltd. The LH2 group samples (LH2_1 to LH2_10) were collected from the LH1 group after one passage. Meanwhile, the HH group samples, designated HH_1 to HH_10, consisted of 5th instar larvae sourced from long-term cultures of eggs from Guangxi that were grown in Qinghai. All the black soldier fly samples were raised on the same feed regimen. The hatching feed comprised a 1∶1 mixture of corn meal and corn starch, while the larval feeding diet consisted of a 1∶6 mixture of wheat bran and kitchen waste.

After starving all larvae for 24 hours, we proceeded to sterilize the surfaces of the entire insect body. We wiped the larvae with 70% alcohol for 30 s, followed by a one-minute soak in 0.25% sodium hypochlorite, and rinsed them three times with sterile water to eliminate any external contaminants. Following the disinfection process, all samples were sent to Shanghai Majorbio Bio-Pharm Technology Co., Ltd. in Shanghai, China, for gut dissection.

1.2   DNA extraction

Total genomic DNA was extracted from intestinal samples using the TIANamp Stool DNA Kit (Tiangen Biochemical Technology, Beijing). DNA concentration and and purity were quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA).

1.3   PCR amplification

For the V4~V5 region of the 16S rRNA gene in the bacterial sample, the upstream primer 515F(GTGCCAGCMGCCGCGG) (Caporaso et al., 2011) and the downstream primer 907R (CCGTCAATTCMTT TRAGTTT) (Armitage et al., 2012) were selected for amplification. The PCR reaction system employed in this study comprised a total volume of 20 μL, which included 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mmol/L dNTPs, 0.8 μL of each positive and negative primer (5 μM), 0.4 μL of FastPfu polymerase, 0.2 μL of BSA, and 10 ng of template DNA, with the volume adjusted to 20 μL using ddH2O. The reaction conditions consisted of an initial denaturation at 95℃ for 3 minutes, followed by 27 cycles of 95℃ for 30 seconds, 45℃ for 30 seconds, and 72℃ for 45 seconds. A final extension was performed at 72℃ for 10 minutes. PCR products were analyzed using 2% agarose gel electrophoresis, and the purified products were sequenced using the Illumina MiSeq platform at Shanghai Majorbio Bio-Pharm Technology Co., Ltd.

1.4   Bioinformatics and statistical analysis

The data analysis for this study was performed using the Majorbio Cloud Analysis Platform (https://cloud.majorbio.com) provided by Shanghai Majorbio Bio-Pharm Technology Co., Ltd.

The paired-end (PE) reads obtained from sequencing were initially spliced based on overlapping regions. This was followed by quality control and filtering of the sequences. Clustering was conducted using USEARCH (v11.0, http://drive5.com/uparse/) with a 97% similarity threshold to generate operational taxonomic units (OTUs) (Edgar, 2013). For the taxonomic analysis of the 97% OTU representative sequences, the RDP classifier (v 2.11, http://sourceforge.net/projects/rdp-classifier/) was used, employing a Bayesian algorithm (Wang et al., 2007). The taxonomic assignments were then compared against the Silva v138 database (http://www.arb-silva.de) (Quast et al., 2013), and the species composition was analyzed at both the phylum and genus levels.

The Qiime platform (http://qiime.org/scripts/assign_taxonomy.html) was utilized to analyze the richness (ACE and Chao indices) and diversity (Shannon indices) of the gut microbiota in each sample (Bolyen et al., 2019). A statistical t-test was conducted to determine whether significant differences existed among the index values across the four groups, with the false discovery rate (FDR) applied to evaluate the significance of these differences. The beta diversity distance matrix was computed using Qiime (v2020.2.0). To visualize the differences among the four sample groups, principal component analysis (PCA) and principal coordinates analysis (PCoA) were performed based on the Bray-Curtis distance algorithm, using R (version 3.3.1). The vegan package (version 2.4.3) in R facilitated non-metric multidimensional scaling (NMDS) analysis and visualization (Kambura et al., 2016). To test for significant differences among groups at the phylum classification level, the stats package in R (version 3.3.1) and the scipy package in Python (version 1.0.0) were utilized, identifying four species that displayed significant differences among the groups. Additionally, the LEfSe software (http://huttenhower.sph.harvard.edu/lefse) was employed to perform linear discriminant analysis (LDA) based on the taxonomic classifications of samples under various conditions, allowing for the identification of communities or species that significantly differentiated the four groups of samples (Sun et al., 2016), with the default LDA score threshold was set at 4.0. The taxonomic level from phylum to species was selected for LEfSe analysis, and the multi-group comparison strategy selected was all against all, and the relative abundance in each sample was thoroughly analyzed.

PICRUSt2, which incorporates the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, was utilized to predict the categories and abundance of KEGG pathways (Kanehisa et al., 2012; Douglas et al., 2020). Specifically, this study employed PICRUSt2 to forecast data related to KEGG metabolic pathways and clusters of orthologous groups (COG), using operational taxonomic unit (OTU) sequences along with their respective abundances as input. The proportions of COG functional categories across different sample groups were visualized using column charts, while the distribution of functional abundance at level 1 and level 2 of KEGG pathways was illustrated through heat maps. Additionally, the Kruskal-Wallis test was conducted to evaluated differences in the variables (KEGG pathways) of gut microbiota between high-altitude and low-altitude areas.

2.   Results and analysis

2.1   Splicing and assembly of the 16S rRNA sequence of the microbiota present in the gut of BSFL

Based on Illumina sequencing, the 16S rRNA gene was sequenced for the gut microbiota of the fifth-instar larvae from the LL, LH1, LH2, and HH groups. A total of 2 313 150 optimized sequences were obtained from the 40 gut samples of the black soldier fly larvae, with an average sequence length of 376 bp. After conducting quality control, this research retained 1 618 200 valid sequences. The sequence reads were clustered at a similarity threshold of 97.0%, as shown in attached table 1. The annotation results, based on operational taxonomic units (OTUs), revealed that a total of 788 OTUs were identified across the 40 samples. These OTUs were classified into 15 phyla, 32 classes, 86 orders, 151 families, 281 genera.

2.2   Sequencing depth and α-diversity analysis of the midgut microbiota community of BSFL

As illustrated in the rank abundance curve (Fig. 1-A), the horizontal coordinate curve became relatively flat with an increasing number of species, indicating that the species composition of the samples was relatively uniform. This reflected the diversity of gut microbiota among the different samples. The Shannon diversity index dilution curve (Fig.1-B) and the rarefaction curve (Fig.1-C) both trended towards smoothness at the end, suggesting that the sequencing sample size was sufficient. The α diversity of the gut microbiota from the four groups was analyzed using the Shannon index, Chao index, and ACE index, and Sobs indices. Figure 2 demonstrated the significant differences in these diversity indices at the OTU level, while Table 1 provided the corresponding values for each sample. From the results presented in Fig.2-A, there were significant differences in the Shannon index between the LL group and the other groups, with the Shannon index of the LL group being significantly higher than those of the other three groups (P < 0.001). Both the ACE (Fig.2-B) and Chao indices (Fig.2-C) were significantly different between the LL group and the other groups, with the LL group showing significantly higher values than the other three groups (P < 0.001). The Sobs index (Fig.2-D) for the LL group was significantly higher than that of the LH1 and LH2 groups (P < 0.01), and it also exhibited a significant difference compared to the HH group (P < 0.05). Overall, the species abundance and diversity of gut microbiota in the LL group were found to be greater than those in the other three groups. Specifically, the gut microbiota richness and diversity of BSFL in the LL group were the highest, while the lowest microbial richness was observed in the HH group and the lowest diversity was noted in the LH2 group.

Fig.  1  OTU analysis and richness evaluation of the black soldier flies

Note: Rank abundance curves (A), Shannon index curves (B), Dilution curves (C) of gut microbiota communities at OUT level in each group of black soldier fly larvae (BSFL).

下载: 全尺寸图片

Fig.  2  α-diversity analysis of BSFL gut microbiota in each group

Note: α-diversity of BSFL gut microbiota in each group, assessed using the Shannon indices (A), ACE indices (B), Chao indices (C), and the sobs indices (D). The data presented in the figure are expressed as mean ± SD. Statistical significance is indicated as follows: *** represents a very significant difference between samples (P < 0.001).

下载: 全尺寸图片

2.3   Analysis of bacterial β‑diversity in the gut of BSFL

Hierarchical clustering analysis of operational taxonomic units (OTUs) was performed on the gut microbiota of the four groups of BSFL samples using the UPGMA algorithm to create a dendrogram. As shown in Fig.3-A, the LL group consistently clustered together into a single distinct cluster, while the other three groups of BSFL samples formed a separate cluster. Within the LH1 group, nine samples clustered together, with the exception of LH1_3. The LH2 and HH groups also clustered together, though LH2_1 and LH2_3 stood apart. These results indicated that the gut microbiota of the LL group was significantly different from that of the other three groups, while there was no significant difference among the individuals within the LL group itself. The gut microbial composition of the BSFL in the LH1 group differed from that of the LH2 and HH groups, while the microbial profiles in the LH2 group were similar to those in the HH group. The clustering of LH1_3, LH2_1, and LH2_3 may be attributed to individual variability among the black soldier flies.

Fig.  3  β-diversity analysis of gut microbiota in the black soldier fly

Note: UPGMA clustering tree (A) of gut microbes in black soldier fly larvae (BSFL) across each group based on OTU levels. PCA (B), PCoA (C), NMDS (D) analysis of BSFL gut microbiota in each group utilizing the Euclidean distance algorithm and based on the Bray-Curtis dissimilarity measure.

下载: 全尺寸图片

The results from the PCA (Fig.3-B) and PCoA (Fig.3-C) analyses indicated a clear separation of the sample points for the LL group from those of the other three groups. The LH1, LH2, and HH groups exhibited closer proximity to one another, with overlap observed between the sample points of the LH2 and HH groups. These findings demonstrated that the intestinal bacterial composition of the LL group was significantly different from that of the other three groups, while the compositions of the LH2 and HH groups were highly similar. Further analysis using NMDS (Fig.3-D) revealed a stress value of 0.113 (less than 0.2), suggesting that the NMDS analysis accurately reflected the degree of differences among the samples. The LL group was distinctly separated from the other three groups, while the sample points of the LH1 and LH2 groups were close. Additionally, the HH group and LH2 group exhibited very close annotation distances, indicating that the gut microbiota composition of the BSFL in the LL group was markedly different from those in the other groups, and that the compositions in the HH and LH2 groups were similar. These results were consistent with those from the PCA and PCoA analyses, which indicated a relatively stable gut microbiota structure across the four groups. Overall, these findings confirmed that the differences between samples in distinct groups were greater than those observed within the same group.

2.4   Comparison of species composition and abundance of microbiota identified in the gut of black soldier fly larvae across different groups

As illustrated in the figure (Fig.4), the predominant bacterial phyla identified in the gut microbiota of black soldier fly larvae (BSFL) in the LL group were Bacteroidota (39.14%), Actinobacteriota (29.85%), and Proteobacteria (16.96%). In the LH1 group, the predominant phyla were Firmicutes (52.03%), Proteobacteria (21.15%), and Bacteroidota (13.63%). For the LH2 group, the top three phyla were Firmicutes (40.29%), Bacteroidota (36.32%), and Proteobacteria (18.87%). Finally, in the HH group, the dominant phyla were Bacteroidota (41.63%), Actinobacteriota (28.61%), and Proteobacteria (15.36%). At the genus level, the study highlighted the top ten bacterial genera for each group, as shown in Figure 5. In the LL group, the dominent genera were Empedobacter (17.21%), Brevibacterium (15.14%), and Actinomyces (10.98%). For the LH1 group, the predominant genera included Enterococcus (47.32%), Actinomyces (12.49%), and Dysgonomonas (7.62%). In the LH2 group, the top three genera were Dysgonomonas (35.96%), Enterococcus (17.51%), and Weissella (15.22%). Lastly, in the HH group, the leading genera were Dysgonomonas (34.33%), Corynebacterium (21.25%), and Enterococcus (8.73%).

Fig.  4  Relative abundance of the top 10 bacteria phyla of gut microbiota in the fifth-instar larvae of Hermetia illucens

下载: 全尺寸图片

Fig.  5  Relative abundance of the top 10 bacteria genera of gut microbiota in the fifth-instar larvae of Hermetia illucens

下载: 全尺寸图片

2.5   Analysis of different gut microbiota between groups

Significance testing of gut microbiota across the LL, LH1, LH2, and HH groups was conducted at the phylum level (Fig. 6). The results revealed significant differences in the abundance of Bacteroidota, Campilobacteriota, and Patescibacteria among the four groups (P < 0.01). Additionally, extremely significant differences were observed in Firmicutes, Actinobacteriota, Fusobacteriota, and Desulfobacterota among the groups (P < 0.001).

Fig.  6  Significance test of differences between groups at the phylum level

Note: ** indicated P < 0.01, and *** indicated P < 0.001.

下载: 全尺寸图片

LEfSe analysis was performed to assess the differences in intestinal flora among the four groups, with a threshold of LDA > 4.0. The results indicated significant changes in the intestinal flora at the class, order, family, genus, and species classification levels, particularly within the LH1, LH2, and HH groups (Fig.7). At the class level, Bacilli was significantly enriched in the LH1 group. At the order level, Bacillales and Orbales were significantly present in the LH1 group, while Corynebacteriales was significantly enriched in the HH group. At the family level, Enterococcaceae, Planococcaceae, and Orbaceae were significantly represented in the LH1 group, whereas Leuconostocaceae was significantly enriched in the LH2 group. Additionally, Corynebacteriaceae and Beutenbergiaceae were significantly present in the HH group. At the genus level, Enterococcus and unclassified Orbaceae were significantly enriched in the LH1 group, while Corynebacterium and unclassified Beutenbergiaceae were significantly present in the HH group (Fig. 8).

Fig.  7  LEfSe multilevel species classification tree of different treatment groups from phylum to species taxonomic level

下载: 全尺寸图片

Fig.  8  LDA discriminant histogram, the LDA threshold is set to be greater than 4

下载: 全尺寸图片

2.6   Functional prediction of gut microbiota

To gain a deeper understanding of the functional roles of gut microbiota in the four groups of black soldier flies (BSFL), functional abundance predictions were conducted using the PICRUSt2 software based on 16S rRNA sequencing data. The predicted functional profiles were subsequently compared to the COG database. The results at KEGG level 1 indicated that "Metabolism" and "Genetic Information Processing" comprised the largest proportions of functional prediction types (Fig.9). The metabolic functions primarily included amino acid transport and metabolism, carbohydrate transport and metabolism, inorganic ion transport and metabolism, energy production and conversion, coenzyme transport and metabolism, nucleotide transport and metabolism, as well as lipid transport and metabolism. The "Genetic Information Processing" category mainly encompassed translation, ribosomal structure and biogenesis; transcription; and replication, recombination, and repair (Fig.10). As shown in Figure 11, gut microbiota from the larvae of the four groups were annotated to a total of 46 pathways at level 2, with 30 pathways exhibiting significant differences (P < 0.05) (Fig.12). Notably, the pathways related to the metabolism of terpenoids and polyketides, xenobiotics biodegradation and metabolism, metabolism of cofactors and vitamins, and amino acid metabolism in the LL group at low altitude were significantly higher than those in the three high-altitude groups. Conversely, the pathways in the LH1, LH2, and HH groups were significantly enriched compared to the LL group, including glycan biosynthesis and metabolism, nucleotide metabolism, carbohydrate metabolism, and drug resistance: antimicrobial.

Fig.  9  Heatmap of KEGG pathways at level 1 for the four groups

下载: 全尺寸图片

Fig.  10  Functional predictions of Hermetia illucens gut microbiota based on COG database

下载: 全尺寸图片

Fig.  11  Heatmap of KEGG pathways at level 2 for the four groups

下载: 全尺寸图片

Fig.  12  Functional predictions of Hermetia illucens

Note: Highlighting pathways with P < 0.05. The relative abundance expression at Level 2 included the top 30 pathways (Kruskal-Wallis H test; *, 0.01 < P < 0.05; **, 0.001 < P ≤ 0.01; ***, P ≤ 0.001).

下载: 全尺寸图片

3.   Discussion and conclusion

3.1   Effect of altitude on gut microbiota diversity of the black soldier fly

The composition of gut microbiota in insects can alter in response to environmental factors and drug exposure (Ma et al., 2023). Insects residing at high altitudes possess gut microbiota that can effectively decompose food, supply nutrients to the host, and create an intestinal environment resilient to low oxygen levels, cold temperatures, and limited food energy. This adaptability aided their survival in plateau environments (Wang et al., 2023). These environmental stresses may adversely affect the survival of certain gut microbiota. The α-diversity of gut microbiota in the LH1, LH2, and HH groups at high altitude was found to be lower compared with the LL group at low altitude, likely due to environmental influences on gut microbiota composition. However, animals that have acclimated to high-altitude living conditions often display an increased prevalence of specific microbial species within their gut (Qiu et al., 2023), which accounts for the higher abundance of gut microbiota in the HH group compared to the LH1 and LH2 groups.

3.2   The difference of altitude results in the structural changes of gut microbiota of the black soldier fly

Despite the differences in composition and diversity of gut microbiota communities at varying altitudes, the predominant bacterial phyla remained Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota. Although the overall microbiome composition did not change significantly with modifications in the growing environment during the development of black soldier flies, the relative abundances of certain phyla exhibited variability. Notably, Fusobacteriota, Patescibacteria, and Desulfobacterota were exclusively found in the LL group. Research indicated that Patescibacteria primarily survive by adhering to the surface of Actinobacteriota (Wang et al., 2023). As the altitude of the black soldier fly's habitat increased, the abundance of Actinobacteriota in their intestines diminished. It was speculated that these three phyla may have experienced declines in abundance or even extinction due to their inability to thrive in high-altitude conditions, leading to their absence in the intestines of the black soldier flies cultured at higher altitudes.

Differences in the external environment can lead to variations in the microbiota community structure among insects (Zhou et al., 2015). The distinct ecological climates found at high and low altitude areas resulted in significant differences in the gut microbiota structure of the LL group at low altitude when compared to the LH1, LH2, and HH groups at high altitude, with the community composition being more dispersed in the LL group. In contrast, the gut microbiota community structures of the LH1, LH2, and HH groups at high altitude were quite similar, exhibiting a more concentrated community composition as a result of environmental adaptation.

3.3   Mechanism of gut microbiota adaptation to high altitude in black soldier fly

This study hypothesized that the bacterial families that play a significant role in the high-altitude adaptation of the black soldier fly (Hermetia illucens) include Enterococcaceae, Planococcaceae, Orbaceae, Leuconostocaceae, Corynebacteriaceae, and Beutenbergiaceae. Research has identified Enterococcus as the predominant bacterial group in the midgut of the moth Macrocera, with an observed increase in Enterococcus populations positively correlating with elevated expression levels of immune-related genes (Krams et al., 2017). This suggested that Enterococcus may enhances the immune function of the black soldier fly (Kathrin et al., 2016). Additionally, research has indicated that flies inoculated with Leuconostocaceae demonstrate a preference for carbohydrate-rich foods (Wong et al., 2017). Therefore, this research hypothesized that Leuconostocaceae may be associated with enhanced carbohydrate metabolism in black soldier flies at high altitude. Hamada described Beutenbergiaceae as facultative anaerobes that are capable of utilizing various carbon sources (Groth et al., 1999). The symbiotic strain YH12T, which was isolated by Fangfang from the intestinal tract of the black soldier fly, is also part of the Beutenbergiaceae family and has been shown to positively promote the growth and development of the black soldier fly (Fang, 2020). Additionally, members of the Corynebacteriaceae family within the gut of the black soldier fly can secrete carbohydrate-degrading enzymes, which facilitate the breakdown and utilization of carbohydrates (Jiang et al., 2019). The gut microbiota of insects significantly impacted the host by influencing nutritional metabolism, immune defense, and enhancing host resistance (Lv et al., 2021). Gut microbiota was involved in regulating various physiological functions, including nutritional metabolism, growth and development, infection protection, neural activity, and behavior in fruit flies. They played a crucial role in the life cycle and physiological functions of mosquitoes, assisting in food digestion, regulating metabolism and growth, bolstering the immune system, defending against pathogen colonization and invasion, and maintaining beneficial symbiotic relationships (Cirimotich et al., 2011; Wang et al., 2012; Coon et al., 2014; Morgane et al., 2018; Bai et al., 2019; Scolari et al., 2019; Wu et al., 2019; Gao et al., 2021; Feng et al., 2022). Building on previous studies and our findings, it can be inferred that gut microbiota may assist the black soldier fly in adapting to environmental conditions by participating in metabolic processes and pathogen defense. During altitude adaptation, insects typically lower their overall metabolic rate to lower their oxygen requirements (Moore, 2017). Although the primary metabolic pattern may shift, the overall metabolism of black soldier flies at high altitude may be reduced.

3.4   Conclusion

The gut microbiota play a crucial role in various aspects of biological activity, including adaptation to high altitude. The gut microbiota of the black soldier fly (Hermetia illucens) adapts to environmental changes by modifying the metabolic capacity and primary metabolic pathways of the host. The altitude of the ecological environment significantly influenced the composition and abundance of gut microbiota, which, in response to environmental stress, regulates the functional metabolism of the microbial community. This study found that the primary metabolic patterns of black soldier flies at high altitude were predominantly focused on carbohydrate metabolism, glycan biosynthesis and metabolism, and nucleotide metabolism. These metabolic adaptations may be associated with the presence of specific bacterial families, including Enterococcaceae, Orbaceae, Leuconostocaceae, Corynebacteriaceae, Planococ-caceae, and Beutenbergiaceae. Furthermore, the study observed the presence of pathogenic bacteria in the intestinal tract of black soldier flies at low altitude, suggesting a potential area for further investigation. The interactions between black soldier flies and their gut microbiota are complex and play a significant role in their adaptation to varying environmental conditions.

附录: 附表 1 不同处理组OUT信息

附表 2 黑水虻5龄幼虫肠道微生物16S rRNA序列分析

详细数据见网络版增强出版材料(http://hjkcxb.alljournals.net/)