Comparison of the gut microflora of common kestrel (Falco tinnunculus) according to differences in sex, age, and physiological condition

doi:10.21203/rs.3.rs-1857423/v1

Background

Gut microbes play an important role in animal health, and their compositions and changes can reflect the physical condition of animals. However, the gut microbes of most birds, especially raptors, have been poorly investigated. This study used 16S rRNA gene sequencing technology to determine the gut microbiome of rescued common kestrels, compared the composition and diversity of the gut microbiome of common kestrels under different physiological conditions, and analyzed the effects of sex and age on the gut microbes of common kestrels.

Results

The results showed that Proteobacteria and Firmicutes were the dominant bacteria at the phylum level among the gut flora of kestrels. Difference in the physiological condition of kestrels exerted a significant influence on their gut microbe composition. Specifically, compared with the healthy group, the abundance of Lactobacillus in the fracture group increased, and the interaction between functionally similar flora was strengthened; in the pneumonia group, the abundance of potentially pathogenic microorganisms such as Clostridium_sensu_stricto_1 and Paeniclostridium was significantly enhanced (P < 0.05). Moreover, the interaction of differences in physiological condition and sex had a significant impact on the abundance of Proteobacteria and Actinobacteria in the guts of kestrels. In addition, the abundance of Acinetobacter was significantly affected by age, and with increasing age, its abundance also increased.

Conclusions

This study showed that sex, age and physiological condition affected the composition and diversity of the gut microbiome of common kestrels, and specific microorganisms changed in abundance in individuals with bacterial pneumonia or fractures, which provides a new perspective for the disease diagnosis and treatment of wild common kestrels.

Common kestrel (Falco tinnunculus)

health status

gut microbes

biodiversity

16S rRNA gene sequencing

Gut microbes are an important portion of microorganisms in the host that affect the host's physiological processes, such as nutritional metabolism, vitamin synthesis and immune function, and play a vital role in maintaining the balance of host physiological homeostasis [1–4]. Gut microbes interact with host immune cells to form a complex interaction network and affect the systemic inflammatory response [5]. Short-chain fatty acids (SFCAs) produced by gut microbes are transported to different parts of the host through the blood circulation to affect the recruitment and vitality of host immune cells to restrain its inflammatory response [6]. On the other hand, endotoxin produced by gut microbes stimulates host immune cells to release immune factors and promote their inflammatory response [7, 8].

In the past, the relationship between gut microbes and diseases has been widely studied in humans, but in recent years, the important role of gut microbes in monitoring wildlife diseases has been gradually demonstrated [9]. In a study with Moschus berezovskii, the intestinal microbial composition under bacterial pneumonia was revealed, and the potential pathogenic bacteria causing bacterial pneumonia were identified, which may provide a new direction for the treatment of diseases in Moschus berezovskii [10]. Clostridium can destroy the intestinal mucosa of animals and thus cause intestinal inflammation [11]. Artificial addition of some probiotics (such as Lactobacillus) to food can enhance the barrier function of the gastrointestinal tract and reduce inflammation [12]. In addition, the diversity and functional landscape of the gut flora of wildlife were revealed in a study with 184 animal species, and microbiota capable of metabolizing toxins were identified in the intestines of scavenging birds, which may contribute to the digestion of intestinal carrion [13].

Raptors occupy a very important position at the top of the food chain in ecosystems, but many of them are still under serious threat worldwide [14]. Each year, during the breeding or migration season, many individuals with mild or serious diseases are rescued. Trauma, such as the fracture, tear, ecclisis and hematoma of wings, is the most common cause of death in raptors [15]. In addition, various organ inflammation is caused by bacterial infection, such as liver, lung and brain inflammation caused by Salmonella [16], whereas Clostridium sordellii and Streptococcus pneumoniae can cause severe lung inflammation [17–19]. Traditional rescue methods focus on the diagnosis and treatment of specific diseases, ignoring the impact of a large number of gut microbes on the host. Most studies have confirmed that disorders of gut microbes are related to a variety of diseases, such as severe bowel disease, bacterial pneumonia, asthma, and obesity [20–23]. Microorganisms in the gut can affect lung, kidney and brain diseases through the so-called "gut-lung axis", "gut-kidney axis", and "gut-brain axis" [24–26]. For example, disorders of gut microbes caused by external factors (such as antibiotic intake) can increase the probability of suffering from respiratory diseases or aggravate lung inflammation. This mode of action is achieved by microbes in the gut with the help of the "gut-lung axis" pathway [27, 28], indicating that gut microbes are closely related to the physiological state of the host [29].

The common kestrel (Falco tinnunculus) is a small raptor listed as a Class-II protected animal in China, and it is one of the raptors rescued in the highest numbers by the China Wildlife Rescue Center and other wildlife conservation organizations. In previous studies of raptors, the changes in gut microbes were explored from the perspectives of food [30] and developmental stage [31], while the impact of disease on the intestinal microorganisms of raptors was ignored. Open fracture and bacterial infection are the main injury and disease, respectively, in the rescued kestrels [16]. In this study, 16S rRNA high-throughput sequencing technology was used to study the gut microbes of wild kestrels rescued by the Beijing Wildlife Rescue Center to explore the composition and diversity of gut microbes under different physiological conditions. The results can provide a scientific basis for the diagnosis of intestinal diseases of rescued kestrels and lay a foundation for improvements in their health management methods under caged conditions to help them return to nature.

Validation of the 16S rRNA sequencing data of the fecal samples

A total of 27 fecal samples were collected, with 17, 5 and 5 from the healthy group (HG), fracture group (FG) and pneumonia group (PG), respectively. All samples were subjected to 16S rRNA gene sequencing, and after quality control, a total of 2,614,262 high-quality sequences were obtained, of which 2,603,251 were valid sequences, accounting for 99.58% of the total high-quality sequences. An average of 96,417 valid sequences were obtained for each sample, indicating that the sequencing data were sufficient to cover most of gut microbes. It can be seen from the dilution curve (Fig. 1a) that the amount of sequencing data was sufficient to reflect the species diversity in the sample and ensure the reliability of subsequent analyses.

Operational taxonomic unit (OTU) clustering of nonduplicate sequences was performed based on a similarity set at 97%, and a total of 1,111 OTUs were selected as representative sequences, of which 1,097, 905 and 880 OTUs were obtained in the HG, FG and PG, respectively. There were 727 OTUs shared by all three groups; 55 were unique to the HG, but only 10 and 2 were unique to the FG and PG, respectively (Fig. 1b). The abundance of OTU1, OTU2, OTU3, OTU6, OTU9, OTU5, OTU7 and OTU13 in the HG was higher, and thus, they were core OTUs. The number of core OTUs in the FG and PG was small, mainly OTU1, OTU2 and OTU3 (Fig. 1c).

Gut microbe compositions of rescued common kestrels under different physiological conditions

A total of 24 phyla, 51 classes, 124 orders, 221 families, 484 genera, and 561 species of microbes were detected among all of the fecal samples (Table 1). Proteobacteria (42.38%), Firmicutes (39.78%), Actinobacteria (6.06%), and Bacteroidetes (5.83%) were the dominant phyla, and Escherichia-Shigella (21.21%), Clostridium_sensu_stricto_1 (11.64%), Paeniclostridium (7.21%), Lactobacillus (6.73%), and Psychrobacter (2.35%) were the dominant genera.

Table 1

Data for the gut microbial composition of common kestrels under different physiological conditions
Sample ID	Kingdom	Phylum	Class	Order	Family	Genus	Species
HG10*	1	17	34	88	158	325	359
HG11	1	20	38	91	152	302	342
HG13	1	18	33	79	137	262	290
HG16	1	16	28	70	128	252	287
HG17	1	18	37	100	167	323	359
HG20	1	17	32	79	149	294	331
HG21	1	16	32	87	152	302	342
HG27	1	17	31	63	112	232	251
PG28	1	17	36	80	138	257	277
FG29	1	19	37	78	135	250	280
HG30	1	16	28	66	115	225	254
FG31	1	18	37	88	153	324	364
HG32	1	14	27	62	108	202	226
HG33	1	15	29	73	132	245	263
HG34	1	19	44	98	171	328	364
PG35	1	15	29	78	139	259	294
HG36	1	17	38	90	155	278	304
PG37	1	15	27	65	112	210	223
HG38	1	17	33	85	146	278	299
FG43	1	18	37	87	154	312	348
PG47	1	14	25	54	96	196	217
PG48	1	12	22	60	113	226	254
HG51	1	19	37	81	142	289	323
HG54	1	16	34	79	129	256	284
FG56	1	18	34	82	142	282	316
HG58	1	17	37	87	148	313	355
FG60	1	17	33	82	143	285	316
Total	1	24	51	124	221	484	561
*FG, HG, and PG, fracture, healthy, and pneumonia groups, respectively.

At the phylum level, there were seven phyla with a relative abundance higher than 1% in the HG (Proteobacteria, 46.41%; Firmicutes, 33.67%; Actinobacteria, 6.80%; Bacteroidetes, 5.58%; Acidobacteria, 2.47%; Epsilonbacteraeota, 1.41%; and Verrucomicrobia, 1.16%), which accounted for 97.50% of the total bacteria. In the FG, there were six phyla with a relative abundance of more than 1%, including Firmicutes (48.88%), Proteobacteria (33.76%), Bacteroidetes (8.84%), Actinobacteria (3.24%), Cyanobacteria (1.47%), and Acidobacteria (1.45%), and they accounted for 97.64% of the total. In the PG, the relative abundances of the four phyla (Firmicutes, 51.58%; Proteobacteria, 36.08%; Actinobacteria, 5.58%; and Bacteroidetes, 4.82%) were higher than 1%, accounting for 98.06% of the total (Fig. 2a).

At the genus level, there were eight genera with a relative abundance exceeding 2% in the HG (Escherichia-Shigella, 20.45%; Clostridium_sensu_stricto_1, 9.08%; uncultured_bacterium_ f_Enterobacteriaceae, 6.59%; Lactobacillus, 5.02%; Paeniclostridium, 4.91%; Psychrobacter, 3.39%; Romboutsia, 2.55%; Acinetobacter, 2.27%; and Oceanisphaera, 2.07%), and these genera accounted for 68.73% of the total bacteria. In the FG, there were also eight genera with a relative abundance greater than 2%, and they were Escherichia-Shigella (23%), Lactobacillus (22.31%), Clostridium_sensu_stricto_1 (6.53%), Enterococcus (4.7%), Bacteroides (3.61%), Paeniclostridium (3.04%), Sphingomonas (2.57%), and Catellicoccus (2.09%), accounting for 77.43% of the total. In the PG, five genera had a relative abundance higher than 2% (Escherichia-Shigella, 22.32%; Clostridium_sensu_stricto_1, 21.60%; Paeniclostridium, 15.94%; Lactobacillus, 2.68%; and Catellicoccus, 2.10%), accounting for 64.64% of the total (Fig. 2b).

Gut microbial diversity of common kestrels of different sexes and ages under physiological conditions

The abundance and diversity of gut microbes of the rescued common kestrels of different sexes and ages under different physiological conditions were assessed using the abundance-based coverage estimator (ACE), Chao1, Simpson, and Shannon indices. Both the ACE and Chao1 indices of the HG (636.19 and 622.85) were higher than those of the PG (618.17 and 539.79) and FG (590.07 and 618.86). The Simpson index of the FG (0.84) was higher than that of the HG (0.81) and PG (0.81), and the Shannon index of the FG (5.10) was higher than that of the HG (4.55) and PG (4.19). Nevertheless, the differences in physiological condition, sex and age had no significant effects on the ACE, Chao1, Simpson, or Shannon indices (P > 0.05, Table 2).

Table 2

Effects of differences in physiological condition, sex and age on the gut microbe abundance and diversity of rescued common kestrels.
	ACE		Chao1		Simpson		Shannon
	χ²	P	χ²	P	χ²	P	χ²	P
Physiological condition	0.299	0.861	3.681	0.159	0.193	0.908	0.761	0.683
Sex	2.799	0.094	0.162	0.688	0.423	0.515	0.081	0.776
Age	3.821	0.148	5.703	0.058	0.674	0.714	1.238	0.539

Nonmetric multidimensional scaling (NMDS) analysis based on the unweighted UniFrac distance showed that microbial communities were significantly separated at both the phylum and genus levels under the three different physiological conditions (PERMANOVA test: R² = 0.173, P = 0.042 at the phylum level, Fig. 3a; R² = 0.132, P = 0.032 at the genus level, Fig. 3b). However, both sex and age and their interaction exhibited no significant effect on the composition of the microbial community (P > 0.05, Table 3).

Table 3

Significant differences of PERMANOVA test within and between different groups with different genders and ages under physiological status at the phylum and genus levels.
	Phylum			Genus
	F	R²	P	F	R²	P
Physiological status	2.425	0.173	0.042*	1.841	0.132	0.032*
Gender	0.884	0.032	0.498	1.478	0.053	0.472
Age	0.677	0.048	0.685	1.181	0.085	0.689
Physiological status × Gender	1.420	0.102	0.218	0.779	0.056	0.215
Physiological status × Age	0.566	0.061	0.826	0.985	0.106	0.818
Gender × Age	1.175	0.084	0.332	0.927	0.066	0.315

Effects of different physiological status, gender and age on the abundance of dominant microbes at phylum and genus levels

At the phylum level, sex (GLM: χ²₁ = 4.368, P = 0.037) and the interaction between sex and physiological condition (χ²₂ = 78.149, P = 0.017) significantly influenced the abundance of Proteobacteria, while differences in physiological condition (χ²₂ = 4.936, P = 0.085) and age (χ²₂ = 1.671, P = 0.434) showed no significant effects. Simple main effect analysis showed that differences in physiological condition significantly affected the abundance of Proteobacteria in female kestrels (χ²₂ = 8.006, P = 0.018, Fig. 4a) but showed no significant effect in male kestrels (χ²₂ = 4.768, P = 0.092). In female common kestrels, the abundance of Proteobacteria in the PG was significantly different from that in the HG (P = 0.019) and FG (P = 0.006), but there was no significant difference between the HG and FG (P = 0.291). The interaction between sex and physiological condition had a significant effect on the abundance of Actinobacteria (χ²₂ = 6.641, P = 0.036), whereas it showed no significant effect on Actinobacteria (P > 0.05). Simple main effect analysis showed that there were also significant differences in the abundance of Actinobacteria in female kestrels between the PG and HG (P = 0.047, Fig. 4b) and in male kestrels between the PG and FG (P = 0.025). The abundance of Firmicutes, Bacteroidetes and Acidobacteria was not affected by physiological condition, sex, or age or the interaction between physiological condition and sex (P > 0.05).

At the genus level, physiological condition had significant effects on the abundance of Paeniclostridium (χ²₂ = 7.708, P = 0.021), but it showed no significant effects on other genera (P > 0.05). Post hoc multiple comparative analysis showed that the abundance of Paeniclostridium in the PG was significantly different from that in the HG (P = 0.009) and FG (P = 0.023, Fig. 4c). Age (χ²₂ = 10.339, P = 0.006) and the interaction between physiological condition and sex (χ²₂ = 10.340, P = 0.006) significantly affected the abundance of Acinetobacter, but they showed no significant effects (P > 0.05) on other genera. Post hoc multiple comparative analysis also showed that the abundance of Acinetobacter in juveniles was significantly different from that in adults (P = 0.001) and subadults (P = 0.019). Simple main effect analysis showed that physiological condition significantly affected the abundance of Acinetobacter in female kestrels (χ²₂ = 5.738, P = 0.057), the abundance of Acinetobacter in the PG was significantly different from that in the HG (P = 0.020) and FG (P = 0.043), and there was no significant difference between the HG and the FG (P = 0.973, Fig. 4d). The abundances of Escherichia-Shigella, Clostridium_sensu_stricto_1, Lactobacillus, Psychrobacter, Romboutsia, Oceanisphaera, Pseudomonas, and Rhodanobacter were not affected by physiological condition, sex, age or the interaction between them (P > 0.05).

Correlation network analysis of the gut microbes

The top 30 genera in terms of relative abundance were selected, and their Spearman correlation coefficients were calculated and used to analyze their correlation. Genera with highly correlated relative abundance changes were clustered in contiguous modules. The HG formed four modules with 26 nodes (average degree, 5.231; average path length, 2.623; clustering coefficient, 0.679; Fig. 5a). Five genera (RB41, uncultured_bacterium_c_Subgroup_6, Luteolibacter, Rhodanobacter, and Sphingomonas) in module 1 and four genera (Ruminococcaceae_UCG-014, Ochrobactrum, Lactobacillus, and Streptococcus) in module 2 were positively related (|r| ≥ 0.5) to each other, and there were positive correlations (|r| ≥ 0.6) between Clostridium_sensu_stricto_1 and Escherichia-Shigella, Escherichia-Shigella and Paeniclostridium (|r| > 0.5) and between Psychrobacter and Sporosarcina (|r| ≥ 0.6) in module 3 and between Pseudomonas and uncultured_bacterium_f_Enterobacteriaceae (|r| ≥ 0.8) in module 4.

The FG formed three modules with 28 nodes (average degree, 7.143; average path length, 1.340; clustering coefficient, 0.810), and the division of the floral structure was relatively obvious (Fig. 5b). Furthermore, in module-1, six genera (Brevibacterium, Acinetobacter, uncultured_bacterium_c_Subgroup_6, Pseudomonas, uncultured_bacterium_f_Muribaculaceae, Paracoccus) and four genera (Bacillus, Staphylococcus, uncultured_bacterium_f_Enterobacteriaceae, uncultured_bacterium_o_Chloroplast) were positively related to each other (|r| ≥ 0.8). Negative correlations (|r| = 1) were shown between Escherichia-Shigella and Sphingomonas, with the former negatively and the latter positively related (|r| ≥ 0.9) to each of the following six genera: Brevibacterium, Acinetobacter, uncultured_bacterium_c_Subgroup_6, Pseudomonas, uncultured_bacterium_f_Muribaculaceae, and Paracoccus. In module-2, six genera ([Ruminococcus]_torques_group, Phascolarctobacterium, Ruminococcaceae_UCG-005, Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG − 014 and Bacteroides) were positively related (|r| = 1) to each other. Both Faecalibacterium (|r| ≥ 0.9) and uncultured_bacterium_f_Lachnospiraceae (|r| ≥ 0.6) were positively related to [Ruminococcus]_torques_group, Phascolarctobacterium, Ruminococcaceae_UCG-005, Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG-014 and Bacteroides. Clostridium_sensu_stricto_1 was negatively correlated (|r| ≥ 0.9) with [Ruminococcus]_torques_group, Phascolarctobacterium, Ruminococcaceae_UCG-005, Rikenellaceae_RC9_gut_group, Ruminococcaceae_UCG − 014 and Bacteroides, and there was a negative correlation (|r| =1) between Desulfovibrio and Enterococcus. In module-3, there was a negative correlation (|r| ≥ 0.9) between Lactobacillus and Paeniclostridium and a positive correlation (|r| ≥ 0.9) between Paeniclostridium and Catellicoccus.

The PG formed five modules with 26 nodes (average degree, 3.385; average path length, 3.343; clustering coefficient, 0.583; Fig. 5c). In module 1, Comamonas showed a negative correlation (|r| ≥ 0.9) with uncultured_bacterium_f_Actinomycetaceae and uncultured_bacterium_f_Atopobiaceae, and there were positive correlations (|r| ≥ 0.6) among the five genera (Lactobacillus, Rothia, Neisseria, Veillonella, and Fusobacterium) in module 2 and the five genera (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, uncultured_bacterium_f_Muribaculaceae, Enterorhabdus, and uncultured_bacterium_f_Lachnospiraceae) in module-3. In module-4, Acinetobacter and Psychrobacter showed a positive correlation (|r| ≥ 0.9), and they were negatively correlated (|r| ≥ 0.7) with Clostridium_sensu_stricto_1 and uncultured_bacterium_f_Coriobacteriales_Incertae_Sedis. Myroides and uncultured_bacterium_f_Wohlfahrtiimonadaceae showed a positive correlation (|r| = 1) in module-5.

Functional annotation of the gut microbes

Functional prediction analysis based on the KEGG database showed that gut microbes of wild common kestrels possessed functions related to pathways such as metabolism, environmental information processing, gene information processing, and cell processes, among which functional pathways related to metabolism were significantly enriched (Fig. 6). Differences among the three groups were compared for the functional pathways at level 2. The results showed that individuals with different physiological conditions showed significant differences in the functions of the excretory system and development, and the rest showed no significant differences (P > 0.05, Table 4).

Table 4

The functional composition of gut microbes under different physiological conditions.
Function	χ²	df	P
Global and overview maps	3.204	2	0.202
Carbohydrate metabolism	2.745	2	0.254
Amino acid metabolism	1.966	2	0.374
Membrane transport	0.472	2	0.790
Energy metabolism	2.607	2	0.272
Metabolism of cofactors and vitamins	4.462	2	0.107
Nucleotide metabolism	2.424	2	0.297
Translation	1.763	2	0.414
Signal transduction	2.890	2	0.235
Replication and repair	1.615	2	0.445
Lipid metabolism	0.255	2	0.880
Metabolism of other amino acids	1.245	2	0.536
Folding, sorting and degradation	0.351	2	0.838
Cellular community - prokaryotes	1.335	2	0.512
Xenobiotics biodegradation and metabolism	0.902	2	0.636
Glycan biosynthesis and metabolism	0.389	2	0.822
Metabolism of terpenoids and polyketides	0.994	2	0.608
Drug resistance: Antimicrobial	2.971	2	0.226
Cell motility	4.302	2	0.116
Biosynthesis of other secondary metabolites	5.106	2	0.077
Infectious diseases: Bacterial	1.843	2	0.397
Cancers: Overview	3.320	2	3.320
Endocrine system	2.377	2	0.304
Cell growth and death	2.539	2	0.280
Aging	4.739	2	0.093
Neurodegenerative diseases	1.843	2	0.397
Transport and catabolism	0.900	2	0.637
Endocrine and metabolic diseases	0.887	2	0.641
Nervous system	1.178	2	0.554
Environmental adaptation	5.501	2	0.063
Transcription	1.626	2	0.443
Cancers: Specific types	2.029	2	0.362
Drug resistance: Antineoplastic	1.932	2	0.380
Infectious diseases: Parasitic	0.199	2	0.905
Immune system	3.640	2	0.162
Immune diseases	2.033	2	0.361
Digestive system	1.610	2	0.447
Excretory system	6.111	2	0.047*
Signaling molecules and interaction	1.932	2	0.380
Infectious diseases: Viral	1.301	2	0.521
Circulatory system	2.287	2	0.318
Cardiovascular diseases	1.451	2	0.483
Substance dependence	4.511	2	0.104
Development	16.658	2	0.00024***

A heatmap of the correlation between functional pathways of the dominant phyla at level 2 (Fig. 7a) showed that Firmicutes exhibited a significantly positive correlation with functions such as ‘Cancer: overview’, ‘Transcription’, ‘Replication and repair’, ‘Translation’, and ‘Immune diseases’, and it was significantly negatively correlated with functions such as ‘Global and overview maps’, ‘Amino acid metabolism’, ‘Neurodegenerative diseases’, ‘Circulation system’, ‘Drug resistance: antimicrobial’, and ‘Cancers: specific types’. Proteobacteria showed a significant positive correlation with ‘Signal translation’ and a negative correlation with ‘Transcription’, ‘Replication and repair’, ‘Translation’, ‘Signaling molecules and interaction’, etc. Bacteroidea was significantly positively correlated with ‘Glycan biosynthesis and metabolism’ and ‘Energy metabolism’ and negatively correlated with ‘Signal transmission’, ‘Infectious diseases: bacterial’, ‘Endocrine and metabolic diseases’, ‘Immune diseases’, etc.

According to the heatmap of the correlation between functional pathways of the dominant genera at level 2 (Fig. 7b), Lactobacillus was significantly positively correlated with the functions of ‘carbohydrate metabolism’, ‘transcription’ and ‘immune diseases’ and negatively correlated with those of ‘metabolism of cofactors and vitamins’, ‘cell motility’, ‘neurodegenerative diseases’, ‘cancers: specific types’ and so on. Psychrobacter was significantly negatively correlated with ‘aging’, ‘drug resistance: antineoplastic’, ‘cancers: specific types’, and ‘metabolism of terpenoids and polyketides’, and Paeniclostridium was significantly positively correlated with ‘drug resistance: antimicrobial’, ‘digestive system’, and ‘metabolism of other amino acids’.

The gut microbes of animals are closely related to the health, growth and development of the host [32]. Therefore, revealing the changes in gut microbes in wildlife diseases may provide a new direction for wildlife protection and rescue. The results of the present study show that Firmicutes and Proteobacteria were the dominant phyla in the guts of wild common kestrels and coexist in the gut with Actinobacteria, Bacteroidetes, Acidobacteria, Verrucomicrobia and other bacteria, which is consistent with previous studies [31, 33]. By comparing healthy and diseased individuals, it was found that the composition and diversity of gut microbes might be affected by different physiological conditions. At the same time, physiological condition imbalance had a significant impact on specific microorganisms in the gut of female common kestrels. These findings further show that microbes possess functions related to immunity and metabolism and play an important role in maintaining the dynamic balance of the gut microecosystem and ensuring normal physiological function [5].

Proteobacteria are mostly potential pathogenic bacteria, which are usually related to intestinal ecological disorders, metabolic disorders, immune disorders, and so on [34]. However, in this study, there was no significant difference in the abundance of Proteobacteria among the three groups (i.e., the HG, FG and PG), which may be because the food of all the common kestrels tested are small wild animals (such as rats and small reptiles), among which there are many kinds and complex sources, and many wild animals are carriers of pathogenic microorganisms [35]. The abundance of Firmicutes was higher in the FG and PG, which may be because it is the main bacterial type producing short-chain fatty acids. Numerous studies have confirmed that Firmicutes can produce short-chain fatty acids to reduce inflammation [12, 36].

Escherichia-Shigella, Lactobacillus, Clostridium_sensu_stricto_1, Acinetobacter and other bacterial genera are widely found in the gut of wild birds [37]. In this study, the abundance of Lactobacillus in the FG was higher than that in the HG (Fig. 2). Common kestrels with fractures are prone to wound infections, and endotoxins produced by Lactobacillus have antibacterial activity. For example, Lactobacillus was shown to be able to inhibit Staphylococcus aureus infection in a rabbit fracture infection model [38], which may reflect the potential of probiotics such as Lactobacillus in the treatment of wildlife diseases. The abundance of Paeniclostridium in the PG was significantly higher than that in the HG (Fig. 2b). Studies have proven that the endotoxins of Paeniclostridium isolated from individuals with disease have high virulence. For example, Paeniclostridium sordellii can infect mouse pulmonary vascular endothelial cells and thus is associated with a high mortality rate [17, 39]. In addition, Paeniclostridium may also be related to mastitis, hemorrhagic enteritis, genital gangrene and other diseases of livestock [40, 41]; therefore, it may be related to common kestrel pneumonia. Similarly, the abundance of Clostridium_sensu_stricto_1 in PG was also high (Fig. 2b), which was similar to those reports that its abundance increases significantly when the intestinal microorganisms are out of imbalance [42, 43]. It is speculated that lung inflammation may affect the composition of intestinal microorganisms.

We also examined the effects of sex and age on the composition of microbes and found that neither one had a significant effect on the diversity of gut microbes in any group (P > 0.05, Table 3). For the dominant phyla and genera, the interaction between sex and physiological condition had a great impact on microbial diversity, which was specifically reflected in the significant impact of different physiological conditions on the abundance of Proteobacteria, Actinobacteria and Acinetobacter in females but not in males (Fig. 4). Researchers have found that female raptors are more vulnerable to disease than males [44]; thus, it is speculated that the gut microbes of female individuals are more significantly affected by disease. In addition, studies have found that age has a significant impact on the composition of gut microbes of wild animals [45, 46]. In this study, age had a significant impact on the abundance of Acinetobacter in the gut microbes of wild common kestrels, and its abundance increased with increasing age, which is similar to the findings reported in studies on pied flycatchers (Ficedula hypoleuca) and house sparrows (Passer domesticus) [46, 47]. This may be due to differences in nutritional needs among individuals of different ages.

The metabolic pathways of functional genes of the gut microbes predicted by the PICRUSt2 algorithm in common kestrels are consistent with those predictions reported in other wild animals [48]. That is, gut microbes have functions related to metabolism, environmental information processing, gene information processing, and cell processes. In particular, they were significantly related to the expression of metabolic functions (Fig. 6), indicating the importance of gut microbes in the construction of host metabolic capacity. Our study also found that gut microbes have immune functions. The heatmap shows that Firmicutes and Lactobacillus were significantly positively correlated with immune diseases, implying that Firmicutes and Lactobacillus play important roles in enhancing immunity and resisting diseases, which also suggests the potential of probiotics in wildlife rescue. Psychrobacter and Paeniclostridium were significantly correlated with drug resistance (Fig. 7), indicating that they have genes related to antibiotic resistance genes (ARGs). Studies have shown that the presence of ARGs is related to a high infection rate and mortality [49]. It has been found that infection with Psychrobacter and Paeniclostridium in humans and mammals is related to lung inflammation [39, 50]. It is speculated that the presence of ARGs may be an important factor affecting the high infection rate of Psychrobacter and Paeniclostridium.

The correlation network analysis between microorganisms can provide the basis for the composition of the microbial community and be used to further explore the interaction mode of coexistence or mutual exclusion among various bacterial genera and then deduce whether there is a "cooperative" or "competitive" relationship between different microorganisms. When two species have a strong correlation under the same environmental conditions, the two species have a certain degree of niche overlap, and mutual exclusion may be caused by competition or niche differentiation [51]. The present study shows that the correlation of gut microbial network structure in the HG was more complex; Clostridium_sensu_stricto_1, Escherichia-Shigella, and Paeniclostridium were significantly positively correlated with each other, and these genera had a positive correlation with immune disease-related pathways (Fig. 5a and Fig. 7b). Therefore, the abundance of microbes with similar functions in the gut may change synergistically. The interaction between microbes was stronger in the FG than in the HG, and the division of the flora module was more obvious: [Ruminococcus]_torques_group, Ruminococcaceae_UCG − 005, Rikenellaceae RC9_gut_group, Ruminococcaceae_UCG − 014, etc., had a strong correlation, and these genera belong to Firmicutes and Bacteroidetes. These beneficial bacteria were all related to the pathogenic Clostridium_sensu_stricto_1 and showed a significant negative correlation (Fig. 5b), which indicates that when potential pathogens in the kestrel's gut play a role, the beneficial bacteria maintaining gut balance form a "functional group" to jointly resist the pathogenic bacteria. In the PG, Clostridium_sensu_stricto_1 was negatively correlated with Leucobacter, Psychrobacter, Bacteroides and other genera (Fig. 5c). It is possible that the increase in Clostridium_sensu_stricto_1 occupied the niche of other genera. Both Psychrobacter and Escherichia_Shigella strengthened the interaction with Acinetobacter (Fig. 5a and Fig. 5b). It is speculated that this may also be related to the transfer of ARGs among these pathogenic bacteria [52] or to the survival mechanism by which some pathogenic bacteria borrow certain protein and RNA components from other bacteria to enhance their own reproductive ability [53].

In conclusion, this study proves that sex, age and physiological condition affected the gut microbial composition and diversity of rescued common kestrels. Potential pathogenic microorganisms and probiotics coexisted in the gut of healthy common kestrels to maintain gut homeostasis. The potential pathogenic microorganisms in the intestine of nonhealthy common kestrels increased significantly, and the interaction of specific pathogenic microorganisms was also strengthened, which might change the structure of the gut microbiota.

Animals

The animals used in this study were the rescued common kestrels from the Beijing Wildlife Rescue and Breeding Center from May to June of 2021. The individuals were grouped according to their physiological conditions. After veterinarian examination, individuals without obvious disease characteristics were placed in the HG, those with broken wings in the fracture group (FG), and those with bacterial pneumonia as the pneumonia group (PG). A total of 27 fecal samples were collected, with 17, 5 and 5 from the HG, PG and FG, respectively. The kestrels were divided into three different age groups according to molting cycle status and the shape and wear of the feathers (Table 5), of which 7 were adults, 5 were subadults, and 5 were juveniles. The sex of each kestrel was identified by PCR amplification of DNA from the back feathers using the method reported previously [54], and 13 males and 14 females were identified (Table 5). All individuals were kept in cages separately and fed water, fresh mice or beef every day.

Table 5

Experimental common kestrel individual information.
Individual ID	Condition	Sex	Age
HG10	Healthy	Male	Juvenal
HG11	Healthy	Male	Juvenal
HG13	Healthy	Male	Adult
HG16	Healthy	Male	Subadult
HG17	Healthy	Female	Adult
HG20	Healthy	Female	Juvenal
HG21	Healthy	Male	Juvenal
HG27	Healthy	Female	Adult
PG28	Pneumonia	Female	Juvenal
FG29	Fracture	Female	Subadult
HG30	Healthy	Female	Subadult
FG31	Fracture	Female	Subadult
HG32	Healthy	Male	Adult
HG33	Healthy	Female	Subadult
HG34	Healthy	Male	Juvenal
PG35	Pneumonia	Male	Subadult
HG36	Healthy	Female	Subadult
PG37	Pneumonia	Male	Adult
HG38	Healthy	Female	Subadult
FG43	Fracture	Female	Subadult
PG47	Pneumonia	Female	Subadult
PG48	Pneumonia	Male	Subadult
HG51	Healthy	Female	Subadult
HG54	Healthy	Male	Juvenal
FG56	Fracture	Male	Subadult
HG58	Healthy	Female	Adult
FG60	Fracture	Male	Adult

Collection and preservation of feces

Fecal samples from all individuals were collected immediately after rescue. For sampling, each rescued kestrel was marked and placed into a shaded cage lined with sterile white paper. Immediately after an individual defecated, the feces were collected with sterile cotton swabs and placed into a centrifuge tube. The tube was labeled and stored at -80°C until further use. No less than 2 g of fresh feces was collected in each centrifuge tube.

DNA extraction

Bacterial DNA was extracted from feces using the QIAamp DNA Stool Mini Kit (QIAGEN, Hilden, Germany), and the concentration and integrity of the extracted DNA were determined by nanodrop and agarose gel electrophoreses, respectively. Samples with a concentration greater than 5 ng/µl and a distinct main band on the agarose gel were selected for PCR amplification with the universal primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The 10 µl reaction system was composed of 5 ng DNA, 0.3 µl of each primer (10 µM), 5 µl KOD FX Neo Buffer, 2 µl dNTPs (2 mM), 0.2 µl PCR enzyme (KOD FX Neo), and ddH₂O to 10 µl. PCR cycling conditions included a predenaturation step at 95°C for 5 min, 25 cycles of denaturation at 95°C for 30 s, annealing at 50°C for 30 s, and extension at 72°C for 40 s, followed by an extension step at 72°C for 7 min and then termination at 4°C. The amplified PCR products were detected by electrophoresis using agarose gels at a concentration of 1.8%, and samples with distinct main bands were selected for building the database. The database was then checked by the Qsep-400 method and quantified by Qubit 3.0. Double-end sequencing was performed on the Illumina Novaseq 6000 platform after the library was qualified quantitatively (i.e., concentration ≥ 1 ng/µL, fragment center value 430–530 bp, average value 420–580 bp, peak shape normally distributed, and the fragment being single without heteropeaks).

Sequence processing

Raw reads obtained from sequencing were filtered using Trimmomatic v0.33 software, and then the primer sequences were identified and removed by Cutadapt 1.9.1 software to obtain high-quality clean reads. Sequences from each sample were spliced and then length filtered by Usearch v10 software, and chimeric sequences were identified and removed by UCHIME v4.2 software to obtain the final valid data (effective reads). The sequences were clustered at the 97% similarity level using Usearch v10 software to obtain operational taxonomic units (OTUs). The raw sequences obtained in this study are available through the National Center for Biotechnology Information (NCBI) database (accession number: PRJNA854312).

Species annotation and taxonomic analysis

Taxonomic analysis of OTU sequences was performed using a plain Bayesian classifier with SILVA as the reference database. This generated taxonomic information on the species corresponding to each feature and then allowed the analysis of microbial community composition at different levels, including phylum, class, order, family, genus, and species. Species abundance lists at different taxonomic levels were generated using QIIME2 software, and then the community structure at each taxonomic level was mapped using R software.

Statistical analysis

Species richness and diversity indices, including the ACE, Chao1, Shannon, and Simpson Indices, were calculated using the vegan package [55] in R software (3.6.3) and were used to analyze species diversity and complexity. Unweighted UniFrac distances between samples were calculated using the phyloseq package. Then, differences in gut microbial communities between the three groups of samples at the phylum and genus levels were assessed separately using NMDS analysis, and the significance of differences between the three groups was tested by permutation multivariate analysis of variance (PERMANOVA). Bubble charts, bar charts, Venn diagrams, and heatmaps of variance maps were generated using R software. The effects of differences in physiological condition, sex and age on the richness and diversity indices, dominant phyla and genera abundance were analyzed by a generalized linear model (GLM). In each analysis, the richness and diversity indicators or the abundance of phyla and genera were regarded as response variables, and different physiological conditions, sex and age were regarded as explanatory variables.

A microbial cooccurrence network was constructed based on the Spearman correlation coefficients between the relative abundances of genera, and the association network was visualized using Gephi 0.9.1 software. The function of each OTU was predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) [56]. The predicted functions were then collapsed into hierarchical KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways in the PICRUSt2 pipeline. The annotation of the function of gut microbes of common kestrels was performed according to the KEGG database [57]. A correlation heatmap between microbial taxa and secondary levels of metabolic pathways was plotted using the pheatmap function based on the Spearman correlation coefficient between the relative abundance of bacterial populations and the relative abundance of predicted pathways. A significant difference test was performed between groups with the Kruskal–Wallis test. P values were corrected by the false discovery rate (FDR), and P < 0.05 was considered statistically significant.

16S rRNA

16S ribosomal ribonucleic acid

SFCAs

short-chain fatty acids

KEGG

Kyoto Encyclopedia of Genes and Genomes

PCR

polymerase chain reaction

NCBI

National Center for Biotechnology Information

DNA

deoxyribonucleic acid

OUT

operational taxonomic units

NMDS

nonmetric multidimensional scaling

GLM

generalized linear model

PERMANOVA

permutational multivariate analysis of variance

ARGs

antibiotic resistance genes

Ethics approval and consent to participate

All fecal samples were collected with the permission of Beijing Wildlife Rescue Center. There is no conflict of interest with species conservation guidelines. All procedures related to animal research were approved by the Ethics and Welfare of Experiment Animals Committee affiliated with Beijing Forestry University. All methods were performed in accordance with the guidelines and regulations, and the study is reported in accordance with ARRIVE guidelines.

Consent for publication

Not applicable.

Availability of data and materials

The molecular data generated during the current study are available in the Shortread Archive at GenBank, https://www.ncbi.nlm.nih.gov/sra (SRA BioProject PRJNA854312).

Competing interests

The authors declare that they have no competing interests.

Funding

This work was financially supported by the Research Fund for General Survey of Terrestrial Wildlife Resources (2017HXFWBHQ-SJL-01), Beijing, China.

Authors' contributions

KQZ, JLS, and XXW conceived and designed the experiments. KQZ and XG conducted the experiments. KQZ and JLS analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.

Acknowledgments

We thank the Beijing Wildlife Rescue Center for allowing research on their grounds and all the staff of the rescue center for their support of this study.

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No competing interests reported.

Comparison of the gut microflora of common kestrel (Falco tinnunculus) according to differences in sex, age, and physiological condition

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Results

Validation of the 16S rRNA sequencing data of the fecal samples

Gut microbe compositions of rescued common kestrels under different physiological conditions

Gut microbial diversity of common kestrels of different sexes and ages under physiological conditions

Correlation network analysis of the gut microbes

Functional annotation of the gut microbes

Discussion

Conclusions

Materials And Methods

Animals

Collection and preservation of feces

DNA extraction

Sequence processing

Species annotation and taxonomic analysis

Statistical analysis

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1