Previous Article in Journal
Unique Features of Extremely Halophilic Microbiota Inhabiting Solar Saltworks Fields of Vietnam
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 12
Issue 10
10.3390/microorganisms12101974
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gut Microbial Communities Are Seasonally Variable in Warm-Climate Lizards Hibernating in the Winter Months

by
Xiaming Zhu
1,2,
Neng Jiang
1,
Tingye Mai
3,
Shulin Wu
3,
Yuntao Yao
3,
Yu Du
3,
Chixian Lin
3,
Longhui Lin
4,* and
Xiang Ji
2,3,*
1
Herpetological Research Center, College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
2
Zhejiang Provincial Key Laboratory for Water Environment and Marine Biological Resources Protection, College of Life and Environmental Sciences, Wenzhou University, Wenzhou 325035, China
3
Hainan Key Laboratory of Herpetological Research, College of Fisheries and Life Sciences, Hainan Tropical Ocean University, Sanya 572022, China
4
Herpetological Research Center, College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 311121, China
*
Authors to whom correspondence should be addressed.
Microorganisms 2024, 12(10), 1974; https://doi.org/10.3390/microorganisms12101974 (registering DOI)
Submission received: 3 September 2024 / Revised: 20 September 2024 / Accepted: 25 September 2024 / Published: 29 September 2024
(This article belongs to the Section Gut Microbiota)
Browse Figures
Versions Notes

Abstract

:
Hibernation is an energy-saving and adaptive strategy adopted by a diverse array of animals, rarely including warm-climate species, to survive in the harsh winter environment. Here, we collected large-intestinal microbial samples from two species of warm-climate lizards, one (the Reeves’ butterfly lizard Leiolepis reevesii) hibernating in the winter months and one (the many-lined sun skink Eutropis multifasciata) not doing so, in summer and winter to analyze and compare their microbiota using 16S rRNA gene amplicon sequencing technology. Gut microbiota were seasonally variable in L. reevesii but not in E. multifasciata. The decreased Firmicutes/Bacteroidetes ratio and increased relative abundance of Verrucomicrobia in hibernating butterfly lizards in a state of long-term fasting should help them live through the winter months, as bacteria of the phyla Bacteroidetes and Verrucomicrobia can use host-derived mucin glycans in the absence of dietary substrates. Facultative plant feeding by omnivorous butterfly lizards resulted in a significant increase in the relative abundance of bacteria of the phylum Firmicutes (e.g., Lachnospiraceae) with the ability to degrade plant fibers. This study not only validates the role of gut microbiota in dietary adaptation in lizards but also shows that gut microbial communities are seasonally variable in warm-climate lizards hibernating in the winter months.

1. Introduction

The coevolution of a host and its gut microbiota is determined by numerous internal and external factors [1,2,3]. Diet as an external factor drives the convergent evolution of gut microbiomes [4,5]. For example, the specialized bamboo diet drives the giant panda Ailuropoda melanoleuca and the red panda Ailurus styani to harbor more bacteria related to starch and sucrose metabolism and vitamin B12 biosynthesis than carnivorous counterparts [4]. Diet primarily selects for functional guilds of bacteria (e.g., cellulolytic consortia), while host phylogeny mainly determines the relative abundance of bacteria taxa among vertebrate clades [6]. Seasonal changes in internal and external factors also affect the gut microbial composition and function [7,8,9,10]. For example, freely grazing yaks (Bos grunniens) shift the Ruminococcaceae_UCG-005 enterotype in the warm season (high protein and low fiber diets) to the Akkermansia and uncultured Eubacterium WCHB1-41 enterotype in the cold season (low protein and high fiber diets) [9]. In the plateau pika Ochotona curzoniae, temporal changes in ambient temperature, precipitation, and nutritional composition (crude protein, crude fiber, and polysaccharide) from vegetation drive seasonal dynamics in gut microbiota, and nutritional composition has a higher contribution to gut microbial variation than climatic factors [8].
As an energy-saving and adaptive strategy adopted by a diverse array of animals to survive in the harsh winter environment, hibernation significantly alters the composition of gut microbiota [11,12,13,14,15,16]. For example, bacteria of the phyla Bacteroidetes and Verrucomicrobia are enriched in winter, and bacteria of the phylum Firmicutes are enriched in the active season in small mammals such as the thirteen-lined ground squirrel Ictidomys tridecemlineatus [11,12], the arctic ground squirrel Urocitellus parryii [15], and the greater horseshoe bat Rhinolophus ferrumequinum [16] hibernating in winter, and the relative abundance of the phylum Verrucomicrobia decreases in hibernating brown bears (Ursus arctos) [13]. Hibernation also significantly alters the composition of gut microbiota in ectothermic animals. In the Chinese alligator Alligator sinensis, the relative abundance of bacteria (e.g., species of the genus Bacteroides) of the phylum Bacteroidetes increases in winter because these bacteria can use host-derived mucin glycans and are therefore beneficial for the hibernating host in a state of long-term fasting [17]. In the Mongolian toad Strauchbufo raddei, the phylum Proteobacteria predominates in hibernating individuals, and the increased relative abundances of bacterial genera (e.g., Pseudomonas, Vibrio, and Ralstonia) of the phylum are positively correlated with the metabolites associated with fatty acid metabolism and biosynthesis of unsaturated fatty acids [18]. The bacteria and metabolites enriched in hibernating Mongolian toads are beneficial for them to live through the winter months [18].
Hibernation rarely occurs in warm-climate regions where temperatures are relatively high and stable. Nevertheless, several lines of evidence from a diverse array of animal taxa, including mammals [19,20] and reptiles [21], prove that hibernation does occur in some warm-climate animals. For example, Malagasy lemurs (Cheirogaleus medius) hibernate in tree holes for seven months every year even when ambient temperatures are above 30 °C [19], and clouded monitor lizards (Varanus nebulosus) enter an inactive period when the daily mean temperature is below 22 °C [21].
Here, we focused on two sympatric species of warm-climate lizards, the Reeves’ butterfly lizard Leiolepis reevesii and the many-lined sun skink Eutropis multifasciata. Leiolepis reevesii is a medium-sized (up to 166 mm snout-vent length, SVL) omnivorous agamid lizard hibernating from late November to early March at a mean nest temperature of ~27 °C, with females producing a single clutch of 2–8 eggs per year from April to July [22]. Eutropis multifasciata is a medium-sized (up to 117 mm SVL) insectivorous sincid lizard not hibernating in the winter months, with females giving birth to 2–7 young per year from April to June [23]. Specifically, we collected large-intestinal content samples from these two species in summer and winter, analyzed and compared the seasonality of gut microbial composition and abundance using 16S rRNA gene amplicon sequencing technology, and tested the following two hypotheses: First, gut microbial diversity and composition should differ between L. reevesii and E. multifasciata because of their difference in food habits. Second, gut microbial communities should be seasonally more variable in L. reevesii than in E. multifasciata because of the role of hibernation in altering the composition of gut microbiota.

2. Materials and Methods

2.1. Sample Collection

We collected 17 adult E. multifasciata (four females and four males in December 2020, and five females and four males in August 2021) and 22 adult L. reevesii (four females and seven males in December 2018, and five females and six males in June 2019) from Ledong (18°29′–18°31′ N, 108°47′–108°57′ E), Hainan Island, South China. We dissected lizards on the day they were captured for collection of large-intestine contents, which were individually labeled and then stored at −80 °C for later DNA extraction.
Our work was carried out in compliance with current laws on animal welfare and research in China and approved by the Animal Research Ethical Committee of Hainan Tropical Ocean University (Approval number: IACUC-20180501).

2.2. DNA Extraction and Sequencing

We used the CTAB/SDS method to extract microbial DNA from large-intestinal samples, assessing concentration and purity with Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA) and 1.2% agarose gels. The V3–V4 region of the bacterial 16S rRNA was amplified by PCR using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR reaction mixture contained 5 μL of buffer (5×), 2 μL of dNTPs (2.5 mM), 1 μL of each primer (10 uM), 0.25 μL of Fast pfu DNA Polymerase, 1 μL of DNA template, and 14.75 μL of ddH2O. The PCR thermal cycling conditions were as follows: pre-denaturation at 98 °C for 5 min, denaturation at 98 °C for 30 s, annealing at 53 °C for 30 s, extension at 72 °C for 45 s, reacting for 25 cycles, and finally extending at 72 °C for 5 min. PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). All samples were mixed with an equal molar amount from the purified PCR product of each sample, and a library was prepared using the TruSeq Nano DNA LT Library Prep Kit (Illumina, Sangon Biotech Co., Ltd., Shanghai, China). Purified amplicon DNA libraries were sequenced using a MiSeq Reagent Kit V3 (Illumina, Sangon Biotech Co., Ltd., Shanghai, China) on an Illumina MiSeq platform (San Diego, CA, USA) in accordance with Frasergen Bioinformatics (Wuhan, China).

2.3. Bioinformatics

We used the Quantitative Insights Into Microbial Ecology 2 (QIIME2) 2020.8 (https://qiime2.org/; accessed on 25 November 2023) to process and analyze raw sequences, the CUTADAPT plugin to trim primers, and the DADA2 plugin to filter reads based on quality, merge paired reads, remove chimeras, and assign reads to amplicon sequence variants (ASVs) [24,25]. We finally obtained 2,359,766 (60,506 ± 2019) high-quality reads from the aforementioned 39 lizards: 17 E. multifasciata and 22 L. reevesii. The obtained sequences were searched against the Greengenes 13_8_99% OTU reference database for taxonomic assignment [26] and reads identified as the sequences from archaea, chloroplast, and mitochondria were removed. A phylogenetic tree was constructed using the MAFFT 7.480 alignment algorithm and the FastTree 2.1.10 maximum likelihood estimation [27,28]. To avoid sample deviations, ASVs with more than 20 reads and present in more than two samples were retained. To standardize the ASV abundance information, the sequencing depth was rarefied at 29,217 according to the 95% of the lowest number of sequences of one sample. Alpha and beta diversity metric values were calculated using the “core-metrics-phylogenetic” pipeline through the QIIME2 q2-diversity plugin. A total of 2164 ASVs were obtained after filter and normalization. Rarefaction curves of estimated ASVs and Shannon indexes indicated that sequence depths were sufficient to describe of the bacterial community (Figure S1).

2.4. Statistical Analysis

We used four alpha diversity indexes to assess the community richness (observed ASVs, Faith’s phylogenetic diversity), diversity (Shannon), and evenness (Pielou’s evenness). Two-way ANOVA was used to examine the effects of host species (E. multifasciata versus L. reevesii), season (summer versus winter), and their interactions on alpha diversity indexes. We used Jaccard and unweighted UniFrac distances to assess community membership, taking the presence and absence of microbial lineages into account; we used Bray–Curtis and weighted UniFrac distances to assess community structure, taking the relative abundances of microbial lineages into account. Jaccard and Bray–Curtis distances belong to non-phylogenetic-based distances, and unweighted and weighted UniFarc distances belong to phylogenetic-based distances. The principal coordinates analysis (PCoA) ordinations were used to visualize structural variations in microbial communities. Permutational multivariate analysis of variance (PERMANOVA, permutation = 999) was performed using the adonis function (vegan package in R 4.3.2) [29] to test the effects of host species, season, and their interaction on these two distance matrixes. A beta dispersion test was performed using the betadisper function (vegan package) to test the homogeneity of dispersion between the two species and between the two seasons. Descriptive statistics were expressed as mean ± standard error (SE) and the significance level was set at p = 0.05.
Linear discriminant analysis (LDA) effect size (LEfSe) [30] was performed to compare the relative abundance of taxa (at the phylum and family levels) with total relative abundances > 1% among the different host species × season combinations. Biomarker species in each of the four (2 host species × 2 seasons) combinations were detected by the Kruskal–Wallis test and linear discriminant analysis (LDA > 3 and p < 0.05). Mann–Whitney U test was performed to compare the relative abundance of taxa between summer and winter in each lizard species. At the ASV level, the differentially abundant ASVs were detected using the metagenomeSeq package [31] and visualized as Manhattan plots.
To obtain the best discriminant performance of the taxa between summer and winter, a random forest model was performed at the family and ASV levels using randomForest (ntree = 10,000) package 4.7-1.1 [32]. The cross-validation error curve of the 10-fold cross-validation was performed by the rfcv function (100 iterations) to identify the number of Biomarker taxa.
Co-occurrence networks were constructed based on Spearman’s pairwise correlations of ASVs using the psych package [33]. To reduce the complexity, ASVs with total relative abundances > 0.1% and present in at least three samples in each group were retained. ASV pairs with a significant correlation (|ρ| > 0.7, p < 0.01) were extracted and visualized using the igraph package [34] and the Gephi 0.9.7 software [35].

3. Results

3.1. Microbial Community Diversity

All four alpha diversity indexes were significantly greater in summer than in winter, three (Observed ASVs, Shannon, and Evenness) of the four indexes were significantly greater in E. multifasciata than in L. reevesii, and the season × species interaction was a significant source of variation in two (Shannon and Evenness) of the four indexes (Table 1). The microbial community membership and structure differed between the two species and between the two seasons, and the season × species interaction was a significant source of variation in these two variables (Figure 1A and Figure S2, Table S1). The microbial community membership and structure differed between summer and winter in L. reevesii but not in E. multifasciata (Table S1). Unweighted and weighted UniFrac distances to centroid revealed the following (Figure 1B): First, gut microbiota were more dispersed in L. reevesii than in E. multifasciata (unweighted: F1, 35 = 31.29, p < 0.01; weighted: F1, 35 = 5.22, p = 0.03). Second, gut microbiota were more dispersed in winter than in summer (unweighted: F1, 35 = 4.62, p < 0.05; weighted: F1, 35 = 4.26, p < 0.05). Third, the host species × season interaction was not a significant source of variation in the degree of dispersion (unweighted: F1, 35 = 0.92, p = 0.34; weighted: F1, 35 = 3.94, p = 0.06). Jaccard and Bray–Curtis distances revealed the following: First, gut microbiota were more dispersed in L. reevesii than in E. multifasciata (Jaccard: F1, 35 = 33.61, p < 0.01; Bray–Curtis: F1, 35 = 14.02, p < 0.01). Second, season was not a significant source of variation in the degree of dispersion (Jaccard: F1, 35 = 2.81, p = 0.10; Bray–Curtis: F1, 35 = 2.20, p = 0.15), nor was the host species × season interaction (Jaccard: F1, 35 = 0.96, p = 0.33; Bray–Curtis: F1, 35 = 1.21, p = 0.28).

3.2. Microbial Community Composition

Firmicutes (37.0 ± 2.8% in E. multifasciata and 44.6 ± 5.8% in L. reevesii), Bacteroidetes (33.5 ± 3.5% in E. multifasciata and 25.1 ± 3.9% in L. reevesii), Verrucomicrobia (7.2 ± 1.5% in E. multifasciata and 20.5 ± 4.7% in L. reevesii), and Proteobacteria (16.1 ± 2.1% in E. multifasciata and 8.4 ± 2.4% in L. reevesii) were the top four dominant phyla in both species (Figure 2A). The LEfSe results showed that three of the top four dominant phyla differed among the four host species × season combinations in terms of relative abundance. Specifically, bacteria of the phylum Bacteroidetes were enriched in E. multifasciata in summer (LDA = 4.73, p = 0.04), bacteria of the phylum Firmicutes were enriched in L. reevesii in summer (LDA = 5.07, p < 0.01), and bacteria of the phylum Verrucomicrobia were enriched in L. reevesii in winter (LDA = 5.22, p < 0.01) (Figure 2B,C). In L. reevesii, bacteria of the phylum Firmicutes were more abundant in summer than in winter, and bacteria of the phylum Verrucomicrobia were more abundant in winter than in summer (Mann–Whitney test, both p < 0.01) (Figure 3). The Firmicutes/Bacteroidetes ratio decreased from 6.60 in summer to 1.06 in winter in L. reevesii, and the difference was significant (U = 112, df = 1, p < 0.01); the ratio did not differ between the two seasons in E. multifasciata (U = 49, df = 1, p = 0.24).
Twelve microbial families differed among the four host species × season combinations in terms of relative abundance (Figure 2B). Bacteria of the families Enterococcaceae (LDA = 3.86, p < 0.01) and Porphyromonadaceae (LDA = 4.38, p < 0.01) were enriched in E. multifasciata in summer. Bacteria of the family Veillonellaceae were enriched in E. multifasciata in winter (LDA = 4.10, p < 0.01). Bacteria of the families Clostridiaceae (LDA = 3.84, p < 0.01), Lachnospiraceae (LDA = 4.63, p < 0.01), Peptostreptococcaceae (LDA = 3.39, p < 0.01), Rikenellaceae (LDA = 3.97, p = 0.02), Ruminococcaceae (LDA = 4.70, p < 0.01), and unclassified_o_Clostridiales (LDA = 4.18, p < 0.01) were enriched in L. reevesii in summer. Bacteria of the families Odoribacteraceae (LDA = 4.32, p = 0.01), Verrucomicrobiaceae (LDA = 4.87, p < 0.01), and unclassified_o_Bacteroidales (LDA = 4.17, p = 0.01) were enriched in L. reevesii in winter. Bacteria of the families Odoribacteraceae and Enterococcaceae were more abundant in summer than in winter and bacteria of the family Oxalobacteraceae were more abundant in winter than in summer in E. multifasciata (Mann–Whitney test, all p < 0.04) (Figure 3). Bacteria of the families Lachnospiraceae, Ruminococcaceae, unclassified_o_Clostridiales, Rikenellaceae, Clostridiaceae, and Peptostreptococcaceae were more abundant in summer than in winter, and bacteria of the families Verrucomicrobiaceae, Odoribacteraceae, and Eubacteriaceae were more abundant in winter than in summer in L. reevesii (Figure 3).
The number of ASVs differing between summer and winter in terms of relative abundance was seven in E. multifasciata and 63 in L. reevesii (Figure 4, Table S2). Of the seven ASVs in E. multifasciata, two [ASV101 of the family Bacteroidaceae (phylum Bacteroidetes) and ASV1184 of the family Bacillaceae (phylum Firmicutes)] were enriched in winter, and five [ASV533 of the family Bacteroidaceae (phylum Bacteroidetes), ASV760, ASV955, and ASV1152 of the family Enterobacteriaceae (phylum Proteobacteria), and ASV1367 of the family Lachnospiraceae (phylum Firmicutes) were enriched in summer. Of the 63 ASVs in L. reevesii, 16 of the phyla Bacteroidetes, Firmicutes, and Verrucomicrobia were enriched in winter, and the remaining 47 of the phyla Actinobacteria, Bacteroidetes, and Firmicutes were enriched in summer. ASV348 and ASV1410 of the family Ruminococcaceae (phylum Firmicutes) and ASV1451 of the family Verrucomicrobiaceae (phylum Verrucomicrobia) were most typical in L. reevesii, with ASV348 enriched in winter, ASV1410 in summer, and ASV1451 in winter.

3.3. Biomarker Taxa for L. reevesii in Summer and Winter

The random forest model showed that the accuracy of microbial classification between individuals of L. reevesii in summer and winter was 81.9% at the family level and 90.9% at the ASV level. The minimum cross-validation error was achieved by using the three most relevant families (Lachnospiraceae, Clostridiaceae, and Veillonellaceae) and 25 ASVs (Figure 5A), which were considered biomarker taxa. These three families and 22 of the 25 ASVs all belonged to the phylum Firmicutes (Figure 5B). Of the 22 ASVs, 10 belonged to the family Ruminococcaceae and eight belonged to the family Lachnospiraceae. Of these 28 biomarker taxa, 25 (three families and 22 ASVs of the phylum Firmicutes) showed higher relative abundance in summer, and the remaining three (ASV348, ASV479, and ASV2074) showed higher relative abundance in winter (Figure 5). Based on the Mann–Whitney test and metagenomeSeq, two families (Lachnospiraceae and Clostridiaceae) and 19 ASVs (other than ASV49, ASV312, ASV395, ASV479, ASV2030, and ASV2118) differed significantly in relative abundance between individuals of L. reevesii in summer and winter (Figure 3 and Figure 4).

3.4. Co-Occurrence Networks

Gut microbial co-occurrence networks were more complex in summer than in winter in both species. In summer, the average connectivity degree was 11.8 in E. multifasciata and 9.8 in L. reevesii, and in winter, it was 6.7 in E. multifasciata and 5.9 in L. reevesii. Similarly, the number of connections (links) in the networks was greater in summer (2851 in E. multifasciata and 2067 in L. reevesii) than in winter (1150 in E. multifasciata and 771 in L. reevesii) (Figure 6). Most links (>84%) in the co-occurrence network were positively correlated in L. reevesii, whereas negatively correlated links increased over 23% in E. multifasciata (Figure 6). Firmicutes and Bacteroidetes were the top two dominant phyla within the co-occurrence networks in both species. Seasonal shifts in the taxonomic composition of the networks were evident in L. reevesii, as revealed by the fact that bacteria of the phylum Bacteroidetes accounted for 14.7% in summer and 30.2% in winter.

4. Discussion

Alpha diversity of gut microbiota was higher in E. multifasciata than in L. reevesii, and the two species were separated in summer and winter in terms of beta diversity of gut microbiota based on four beta diversity matrixes (Jaccard, Bray–Curtis, and unweighted and weighted UniFrac distances). These findings suggest that, as in insects [36], sea turtles [37], and woodrats [2], the gut microbiota are species-specific in warm-climate lizards. Alpha diversity of gut microbiota was higher in summer than in winter in both species, with the lowest community diversity and evenness found in L. reevesii in winter. The finding that the alpha diversity of gut microbiota is higher in summer than in winter is consistent with the studies on S. raddei [18] and O. curzoniae [38]. Higher bacterial diversity of gut microbiota in summer may be due to more diverse and easily accessible food items, higher temperatures, and more frequent precipitation in the season. For example, the Shannon index of gut microbiota is positively associated with cumulative rainfall in the Ethiopian gelada Theropithecus gelada [7]. Hainan has only two seasons throughout the year: rainy season (from May to October) and dry season (from November to April) [39]. Higher alpha diversity of gut microbiota in summer in the two lizard species could be at least partially related to more precipitation in the season. Seasonality in beta diversity of gut microbiota has been reported for mammals such as the furry-eared dwarf lemur Cheirogaleus crossleyi [40] and the greater horseshoe bat Rhinolophus ferrumequinum [16] hibernating in winter. Here, seasonality in microbial community membership and structure was detected only in L. reevesii also hibernating in the winter months.
Two phyla, nine families, and 63 ASVs differed in relative abundance between summer and winter in L. reevesii, whereas only three low-abundance families and seven ASVs differed in relative abundance between summer and winter in E. multifasciata. This inter-specific difference provides an inference that gut microbial communities are seasonally less variable in warm-climate lizards such as E. multifasciata not hibernating in the winter months. Less pronounced seasonal variation in gut microbial communities has been reported for several species of warm-climate animals not hibernating in winter, including two spiny lizards of the genus Sceloporus (S. grammicus and S. spinosus) [41] and the François’ langur Trachypithecus francoisi [42]. In T. francoisi, the absence of significant seasonality in gut microbial diversity and relative abundance of high-abundance taxa primarily results from the stability of host activity rhythm and energy metabolism [42].
Bacteria of the phylum Firmicutes and families Clostridiaceae, Lachnospiraceae, Ruminococcaceae, Peptostreptococcaceae, and unclassified_o_Clostridiales of the phylum were significantly enriched in L. reevesii in summer. It is worth noting that bacteria of the families Ruminococcaceae and Lachnospiraceae are dedicated plant fiber degraders with the ability to ferment polysaccharides to generate short-chain fatty acids (SCFAs, e.g., butyrate and acetate) [43,44]. These bacterial taxa are therefore particularly critical for herbivorous or omnivorous animals [45,46,47,48]. For example, bacteria of the families Ruminococaceae and Lachnospiraceae account for 3–30% of rectal and fecal bacterial operational taxonomic units (OTUs) in the green turtle Chelonia mydas [45], and SCFAs are the main product of fermentation of plant material in the large intestine of this omnivorous sea turtle [49]. Relative abundance of bacteria of the family Lachnospiraceae decreases in green turtles fed with 25% plant and 75% seafood feedstuffs compared with those fed with 50% or higher proportions of plant feedstuffs [50]. The Italian wall lizard Podarcis siculus provides an example where the relative abundance of bacteria of the family Peptostreptococcaceae is higher in insectivorous populations than in omnivorous populations [51]. Our unpublished DNA metabarcoding data reveal that E. multifasciata is an insectivorous species and that L. reevesii is an omnivorous species eating not only arthropods but also fresh leaves of the orders Poales, Caryophyllales, Gentianales, and Brassicales. Higher relative abundance of bacteria of the phylum Firmicutes helps L. reevesii degrade plant fibers, further proving the contribution of the microbial community to dietary adaptation in lizards.
Hibernation significantly altered the gut microbial composition in L. reevesii. Firmicutes bacteria are more efficient in dietary caloric absorption than Bacteroidetes bacteria [52,53,54], and SCFAs produced by Firmicutes bacteria can be directly absorbed by the host as an energy source to promote mass gain [55]. Warming reduces the relative abundances of Firmicutes bacteria in many vertebrates [56,57], including the western fence lizard Sceloporus occidentalis [58]. However, the correlation between the relative abundances of Firmicutes bacteria and temperature also can be positive [59,60] or nonsignificant [61] in lizards. Bacteria of the phylum Bacteroidetes can use host-derived mucin glycans in the absence of dietary substrates [11,62]. A high Firmicutes/Bacteroidetes ratio explains weight gain in birds [63] and mammals [64]. In an earlier study on L. reevesii, the Firmicutes/Bacteroidetes ratio was reported to be greater in individuals acclimated to higher temperatures [59]. The Firmicutes/Bacteroidetes ratio is also related to the alteration in host status from the active to the fasting phase of hibernation [12,13,15,62]. Several lines of evidence from the brown frog Rana dybowskii [65], I. tridecemlineatus [12], and U. arctos [13] consistently show that the decreased relative abundance of Firmicutes bacteria and the increased relative abundance of Bacteroidetes bacteria help hosts adapt to long-term fasting during hibernation. In R. dybowskii, for example, the Firmicutes/Bacteroidetes ratio decreases from 4.23 in summer to 0.63 in winter [65]. Here, we found that the Firmicutes/Bacteroidetes ratio reduced in warm-climate lizards hibernating in the winter months, as revealed by the fact that the ratio did not differ between the two seasons in E. multifasciata but decreased from 6.60 in summer to 1.06 in winter in L. reevesii.
Hibernation decreased the relative abundance of bacteria (e.g., those of the families Ruminococcaceae and Lachnospiracea) using dietary glycans and increased the relative abundance of bacteria (e.g., those of the genus Bacteroides) [62] shifting their foraging targets between dietary glycans and host-derived mucin glycans. Hibernation also increased the relative abundance of bacteria (e.g., those of genus Akkermansia) degrading host-derived mucin glycans [11,12,13,15,17,65,66]. Bacteria of the genus Akkermansia are dedicated degraders of host-derived mucin glycans that can grow with mucin as the sole source of carbon and nitrogen [66,67]. In this study, we found that, as in I. tridecemlineatus during hibernation [12], bacteria of the family Verrucomicrobiaceae (with relative abundance mainly determined by the genus Akkermansia) were enriched in hibernating L. reevesii. Previous studies on the Syrian hamster Mesocricetus auratus [14] and U. parryii [15] showed that fasting, instead of low temperature per se, was the major factor determining the composition and metabolic output of gut microbiota during hibernation. The relative abundance of Akkermansia bacteria was higher and the concentration of total SCFAs was lower in active Syrian hamsters fasted for 96 h than in their active counterparts eating normally or dormant counterparts experiencing 9 to 10 hibernation cycles [14]. Gut microbial communities were more similar between ground squirrels shortly after rousing from hibernation (3 d after hibernation without eating) and in bouts of torpor and interbout arousal than to those in summer [15]. Moreover, the concentration of total SCFAs was significantly lower in ground squirrels shortly after rousing from hibernation than in those in summer [15]. The seasonality in gut microbiota found in L. reevesii hibernating (and thus, fasting) from late November to early March provides an inference that long-term fasting during hibernation significantly affects the composition of gut microbiota.
Numerous factors, including diet and temperature, affect the complexity of microbial co-occurrence networks [68,69]. An earlier study on the Brandt’s vole Lasiopodomys brandtii shows that a more complex co-occurrence network of gut microbiota may provide a more flexible microbial metabolic network [68]. As in O. curzoniae [8], co-occurrence networks of the gut microbial community were more complex in summer than in winter in both lizard species studied herein. To obtain more adequate energy from the diet, Brandt’s voles feeding Cleistogenes squarrosa rich in digestible nutrients have more diverse microbial communities and a complex network than those feeding Stipa krylovii poor in digestible nutrients [68]. The pattern of seasonality in the co-occurrence network complexity was similar between E. multifasciata and L. reevesii, whereas the direction of seasonal changes in negatively or positively correlated microbial taxa differed between the two species. Compared with conspecific lizards in summer, we found more positively correlated microbial taxa in co-occurrence networks in winter L. reevesii and more negatively correlated microbial taxa in winter E. multifasciata. Mutualistic or commensal taxa in microbial communities are positively correlated, and competitive taxa are negatively correlated [70]. The gut microbial co-occurrence network was dominant by positively correlated taxa in winter L. reevesii, presumably because long-term fasting during hibernation resulted in resource scarcity and put gut microbiota under high stress. This conforms to the stress gradient hypothesis, which suggests that more positively correlated taxa occur in the microbial co-occurrence network in high-stress environments (e.g., low-level resources and extreme temperatures) [68,71,72,73]. As environmental stress gradually intensifies, the competitive taxa involved in interspecific antagonism are gradually replaced by slow-growing, high-tolerant taxa, or by taxa providing direct benefits to other taxa [74,75]. Under benign environmental conditions, more negatively correlated taxa can enhance the stability of gut microbiota, because the competition between microbial taxa can reduce or minimize the resonance (co-oscillation) effect of environmental interference on microbial communities [76,77,78]. Thus, more negatively correlated taxa explain why the diversity and composition of gut microbiota are seasonally less variable in E. multifasciata.

5. Conclusions

Our study provides evidence that host species, season, and their interaction are significant sources of variation in the gut microbial diversity and composition in warm-climate lizards, allowing us to draw the following conclusions: First, seasonality in gut microbial alpha and beta diversities is evident in L. reevesii but not in E. multifasciata. Second, the Firmicutes/Bacteroidetes ratio significantly decreases and the relative abundance of Verrucomicrobia significantly increases in winter L. reevesii, helping the species adapt to long-term fasting during hibernation because bacteria of the phyla Bacteroidetes and Verrucomicrobia can use host-derived mucin glycans in the absence of dietary substrates. Third, the gut microbial community of summer L. reevesii is primarily dominated by Firmicutes (e.g., Lachnospiraceae) that can decompose plant fibers, and this finding adds evidence for dietary adaptation in omnivorous lizards. Taken together, our results allow the conclusion that gut microbial communities are seasonally more variable in warm-climate lizards hibernating in the winter months. Future work could usefully investigate other lineages of lizards to determine whether our conclusion is generalizable to all warm-climate species and can be used to guide conservation efforts for L. reevesii, an endangered lizard species on the list of the Class II Key Protected Wild Animals in China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms12101974/s1, Figure S1: Rarefaction curves based on observed ASVs (A) and Shannon index (B) for each sample; Figure S2: Results of principal coordinates analysis (PCoA) on gut microbiota based on Jaccard and Bray–Curtis distances; Table S1: Results of the permutational multivariate analysis of variance (PERMANOVA) on gut microbiota based on Jaccard, Bray–Curtis, unweighted UniFrac, and weighted UniFrac distances in two host species of warm-climate lizards, E. multifasciata and L. reevesii; Table S2: Comparison of relative abundance of ASVs between lizards in summer and winter.

Author Contributions

Conceptualization, L.L. and X.J.; methodology, X.Z.; validation, X.Z.; formal analysis, X.Z., N.J., T.M., S.W., Y.Y. and Y.D.; investigation, X.Z., N.J., T.M., S.W. and Y.Y.; resources, C.L. and X.J.; data curation, X.Z.; writing—original draft preparation, X.Z. and X.J.; writing—review and editing, X.Z., N.J., T.M., S.W., Y.Y., Y.D., C.L., L.L. and X.J.; visualization, X.Z.; supervision, L.L. and X.J.; project administration, L.L. and X.J.; funding acquisition, L.L. and X.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant/Award Numbers 32370519 and 31870390), the Zhejiang Provincial Natural Science Foundation of China (Grant/Award Number LY23C030003), and the Finance Science and Technology Project of Hainan Province (Grant/Award Number ZDYF2018219).

Institutional Review Board Statement

Our experimental procedures complied with current laws on animal welfare and research in China, and were approved by the Animal Research Ethics Committee of Hainan Tropical Ocean University.

Data Availability Statement

The raw sequence reads are available in the National Center for Biotechnology Information Sequence Read Archive (https://ncbi.nlm.nih.gov/sra, accessed on 20 May 2024) under BioProject ID PRJNA1113960.

Acknowledgments

We thank Zhongyin Chen and Qize Liu for assistance in the field, and Jinwei Lv, Wanting Wang, Zixuan Zhang, and Yifan Zhao for assistance in the laboratory.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Huang, G.; Wang, L.; Li, J.; Hou, R.; Wang, M.; Wang, Z.; Qu, Q.; Zhou, W.; Nie, Y.; Hu, Y.; et al. Seasonal shift of the gut microbiome synchronizes host peripheral circadian rhythm for physiological adaptation to a low-fat diet in the giant panda. Cell Rep. 2022, 38, 110203. [Google Scholar] [CrossRef] [PubMed]
  2. Weinstein, S.B.; Martínez-Mota, R.; Stapleton, T.E.; Klure, D.M.; Greenhalgh, R.; Orr, T.J.; Dale, C.; Kohl, K.D.; Dearing, M.D. Microbiome stability and structure is governed by host phylogeny over diet and geography in woodrats (Neotoma spp.). Proc. Natl. Acad. Sci. USA 2021, 118, e2108787118. [Google Scholar] [CrossRef] [PubMed]
  3. Zhu, J.; Li, H.; Jing, Z.; Zheng, W.; Luo, Y.; Chen, S.; Guo, F. Robust host source tracking building on the divergent and non-stochastic assembly of gut microbiomes in wild and farmed large yellow croaker. Microbiome 2022, 10, 18. [Google Scholar] [CrossRef] [PubMed]
  4. Huang, G.; Wang, X.; Hu, Y.; Wu, Q.; Nie, Y.; Dong, J.; Ding, Y.; Yan, L.; Wei, F. Diet drives convergent evolution of gut microbiomes in bamboo-eating species. Sci. China Life Sci. 2021, 64, 88–95. [Google Scholar] [CrossRef]
  5. Muegge, B.D.; Kuczynski, J.; Knights, D.; Clemente, J.C.; González, A.; Fontana, L.; Henrissat, B.; Knight, R.; Gordon, J.I. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 2011, 332, 970–974. [Google Scholar] [CrossRef] [PubMed]
  6. Youngblut, N.D.; Reischer, G.H.; Walters, W.; Schuster, N.; Walzer, C.; Stalder, G.; Ley, R.E.; Farnleitner, A.H. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 2019, 10, 2200. [Google Scholar] [CrossRef]
  7. Baniel, A.; Amato, K.R.; Beehner, J.C.; Bergman, T.J.; Mercer, A.; Perlman, R.F.; Petrullo, L.; Reitsema, L.; Sams, S.; Lu, A.; et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 2021, 9, 26. [Google Scholar] [CrossRef] [PubMed]
  8. Fan, C.; Zhang, L.; Jia, S.; Tang, X.; Fu, H.; Li, W.; Liu, C.; Zhang, H.; Cheng, Q.; Zhang, Y. Seasonal variations in the composition and functional profiles of gut microbiota reflect dietary changes in plateau pikas. Integr. Zool. 2022, 17, 379–395. [Google Scholar] [CrossRef]
  9. Guo, N.; Wu, Q.; Shi, F.; Niu, J.; Zhang, T.; Degen, A.A.; Fang, Q.; Ding, L.; Shang, Z.; Zhang, Z.; et al. Seasonal dynamics of diet–gut microbiota interaction in adaptation of yaks to life at high altitude. Npj Biofilms Microbiomes 2021, 7, 38. [Google Scholar] [CrossRef]
  10. Maurice, C.F.; Knowles, S.C.; Ladau, J.; Pollard, K.S.; Fenton, A.; Pedersen, A.B.; Turnbaugh, P.J. Marked seasonal variation in the wild mouse gut microbiota. ISME J. 2015, 9, 2423–2434. [Google Scholar] [CrossRef]
  11. Carey, H.V.; Walters, W.A.; Knight, R. Seasonal restructuring of the ground squirrel gut microbiota over the annual hibernation cycle. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 2013, 304, R33–R42. [Google Scholar] [CrossRef] [PubMed]
  12. Dill-McFarland, K.A.; Neil, K.L.; Zeng, A.; Sprenger, R.J.; Kurtz, C.C.; Suen, G.; Carey, H.V. Hibernation alters the diversity and composition of mucosa-associated bacteria while enhancing antimicrobial defence in the gut of 13-lined ground squirrels. Mol. Ecol. 2014, 23, 4658–4669. [Google Scholar] [CrossRef] [PubMed]
  13. Sommer, F.; Ståhlman, M.; Ilkayeva, O.; Arnemo, J.M.; Kindberg, J.; Josefsson, J.; Newgard, C.B.; Fröbert, O.; Bäckhed, F. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 2016, 14, 1655–1661. [Google Scholar] [CrossRef] [PubMed]
  14. Sonoyama, K.; Fujiwara, R.; Takemura, N.; Ogasawara, T.; Watanabe, J.; Ito, H.; Morita, T. Response of gut microbiota to fasting and hibernation in Syrian hamsters. Appl. Environ. Microbiol. 2009, 75, 6451–6456. [Google Scholar] [CrossRef] [PubMed]
  15. Stevenson, T.J.; Duddleston, K.N.; Buck, C.L. Effects of season, and host physiological state on the diversity, density, and activity of the Arctic ground squirrel cecal microbiota. Appl. Environ. Microbiol. 2014, 80, 5611–5622. [Google Scholar] [CrossRef] [PubMed]
  16. Xiao, G.; Liu, S.; Xiao, Y.; Zhu, Y.; Zhao, H.; Li, A.; Li, Z.; Feng, J. Seasonal changes in gut microbiota diversity and composition in the greater horseshoe bat. Front. Microbiol. 2019, 10, 2247. [Google Scholar] [CrossRef] [PubMed]
  17. Tang, K.; Wang, Z.; Wan, Q.; Fang, S. Metagenomics reveals seasonal functional adaptation of the gut microbiome to host feeding and fasting in the Chinese alligator. Front. Microbiol. 2019, 10, 2409. [Google Scholar] [CrossRef]
  18. Cao, H.; Shi, Y.; Wang, J.; Niu, Z.; Wei, L.; Tian, H.; Yu, F.; Gao, L. The intestinal microbiota and metabolic profiles of Strauchbufo raddei underwent adaptive changes during hibernation. Integr. Zool. 2024, 19, 612–630. [Google Scholar] [CrossRef] [PubMed]
  19. Dausmann, K.H.; Glos, J.; Ganzhorn, J.U.; Heldmaier, G. Hibernation in a tropical primate. Nature 2004, 429, 825–826. [Google Scholar] [CrossRef]
  20. Liu, J.; Karasov, W.H. Hibernation in warm hibernacula by free-ranging Formosan leaf-nosed bats, Hipposideros terasensis, in subtropical Taiwan. J. Comp. Physiol. B 2011, 181, 125–135. [Google Scholar] [CrossRef]
  21. Goodyear, J.; Gale, G.A.; Waengsothorn, S.; Strine, C.T. Brumation of the clouded monitor lizard Varanus nebulosus in north-eastern Thailand. Herpetol. Bull. 2022, 159, 12–17. [Google Scholar]
  22. Du, Y.; Lin, C.; Lin, L.; Qiu, Q.; Ji, X. Ontogenetic shifts in sexual dimorphism and female reproduction in the Reeves’s Butterfly Lizard Leiolepis reevesii from Hainan, China. J. Herpetol. 2011, 45, 399–405. [Google Scholar] [CrossRef]
  23. Ji, X.; Lin, L.; Lin, C.; Qiu, Q.; Du, Y. Sexual dimorphism and female reproduction in the many-lined sun skink (Mabuya multifasciata) from China. J. Herpetol. 2006, 40, 351–357. [Google Scholar] [CrossRef]
  24. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [PubMed]
  25. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef] [PubMed]
  26. DeSantis, T.Z.; Hugenholtz, P.; Larsen, N.; Rojas, M.; Brodie, E.L.; Keller, K.; Huber, T.; Dalevi, D.; Hu, P.; Andersen, G.L. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 2006, 72, 5069–5072. [Google Scholar] [CrossRef]
  27. Katoh, K.; Misawa, K.; Kuma, K.; Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002, 30, 3059–3066. [Google Scholar] [CrossRef]
  28. Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2-approximately maximum likelihood trees for large alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef]
  29. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package. R Foundation for Statistical Computing. 2002. Available online: https://www.R-project.org/ (accessed on 3 December 2023).
  30. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef]
  31. Paulson, J.N.; Stine, O.C.; Bravo, H.C.; Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 2013, 10, 1200–1202. [Google Scholar] [CrossRef]
  32. Liaw, A.; Wiener, M. Classification and Regression by randomForest. R News 2002, 2, 18–22. [Google Scholar]
  33. Revelle, W. Psych: Procedures for Psychological, Psychometric, and Personality Research. 2023. Available online: https://cran.r-project.org/web/packages/psych/psych.pdf (accessed on 15 January 2024).
  34. Csárdi, G.; Nepusz, T.; Traag, V.; Horvát, S.; Zanini, F.; Noom, D.; Müller, K. Igraph: Network Analysis and Visualization in R. 2024. Available online: https://CRAN.R-project.org/package=igraph (accessed on 15 January 2024).
  35. Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An open source software for exploring and manipulating networks. Proc. Int. AAAI Conf. Web Soc. Media 2009, 3, 361–362. [Google Scholar] [CrossRef]
  36. Malacrinò, A. Host species identity shapes the diversity and structure of insect microbiota. Mol. Ecol. 2021, 31, 723–735. [Google Scholar] [CrossRef]
  37. Scheelings, T.F.; Moore, R.J.; Van, T.T.H.; Klaassen, M.; Reina, R.D. Microbial symbiosis and coevolution of an entire clade of ancient vertebrates: The gut microbiota of sea turtles and its relationship to their phylogenetic history. Anim. Microbiome 2020, 2, 17. [Google Scholar] [CrossRef]
  38. Wang, Y.; Zhou, R.; Yu, Q.; Feng, T.; Li, H. Gut microbiome adaptation to extreme cold winter in wild plateau pika (Ochotona curzoniae) on the Qinghai-Tibet Plateau. FEMS Microbiol. Lett. 2020, 367, fnaa134. [Google Scholar] [CrossRef]
  39. Luo, K. Collection of Hainan Tropical Agricultural Resources Zoning; Science Press: Beijing, China, 1985. [Google Scholar]
  40. Greene, L.K.; Andriambeloson, J.B.; Rasoanaivo, H.A.; Yoder, A.D.; Blanco, M.B. Variation in gut microbiome structure across the annual hibernation cycle in a wild primate. FEMS Microbiol. Ecol. 2022, 98, fiac070. [Google Scholar] [CrossRef]
  41. Hernández, M.; Ancona, S.; Hereira-Pacheco, S.; de la Vega-Pérez, A.H.D.; Alberdi, A.; Navarro-Noya, Y.E. Seasonal dietary changes relate to gut microbiota composition depending on the host species but do not correlate with gut microbiota diversity in arthropod-eating lizards. Mol. Ecol. 2024, 33, e17426. [Google Scholar] [CrossRef]
  42. Liu, H.; Li, Y.; Liang, J.; Nong, D.; Li, Y.; Huang, Z. Evaluation of gut microbiota stability and flexibility as a response to seasonal variation in the wild François’ langurs (Trachypithecus francoisi) in limestone forest. Microbiol. Spectr. 2023, 11, e05091-22. [Google Scholar] [CrossRef]
  43. Moraïs, S.; Mizrahi, I. Islands in the stream: From individual to communal fiber degradation in the rumen ecosystem. FEMS Microbiol. Rev. 2009, 43, 362–379. [Google Scholar] [CrossRef]
  44. Sorbara, M.T.; Littmann, E.; Fontana, E.; Moody, T.U.; Kohout, C.E.; Gjonbalaj, M.; Eaton, V.; Seok, R.; Leiner, I.M.; Pamer, E.G. Functional and genomic variation between human-derived Isolates of Lachnospiraceae reveals inter-and intra-species diversity. Cell Host Microbe 2020, 28, 134–146.e4. [Google Scholar] [CrossRef]
  45. Campos, P.; Guivernau, M.; Prenafeta-Boldú, F.X.; Cardona, K. Fast acquisition of a polysaccharide fermenting gut microbiota by juvenile green turtles Chelonia mydas after settlement in coastal habitats. Microbiome 2018, 6, 69. [Google Scholar] [CrossRef] [PubMed]
  46. La Reau, A.J.; Suen, G. The Ruminococci: Key symbionts of the gut ecosystem. J. Microbiol. 2018, 56, 199–208. [Google Scholar] [CrossRef]
  47. Miyake, S.; Ngugi, D.K.; Stingl, U. Diet strongly influences the gut microbiota of surgeonfishes. Mol. Ecol. 2015, 24, 656–672. [Google Scholar] [CrossRef] [PubMed]
  48. Yuan, M.L.; Dean, S.H.; Longo, A.V.; Rothermel, B.B.; Tuberville, T.D.; Zamudio, K.R. Kinship, inbreeding and fine-scale spatial structure influence gut microbiota in a hindgut-fermenting tortoise. Mol. Ecol. 2015, 24, 2521–2536. [Google Scholar] [CrossRef] [PubMed]
  49. Bjorndal, K.A. Cellulose digestion and volatile fatty acid production in the green turtle, Chelonia mydas. Comp. Biochem. Physiol. 1979, 63, 127–133. [Google Scholar] [CrossRef]
  50. Bloodgood, J.C.G.; Hernandez, S.M.; Isaiah, A.; Suchodolski, J.S.; Hoopes, L.A.; Thompson, P.M.; Waltzek, T.B.; Norton, T.M. The effect of diet on the gastrointestinal microbiome of juvenile rehabilitating green turtles (Chelonia mydas). PLoS ONE 2020, 15, e0227060. [Google Scholar] [CrossRef] [PubMed]
  51. Lemieux-Labonté, V.; Vigliotti, C.; Tadic, Z.; Wehrle, B.; Lopez, P.; Bapteste, E.; Lapointe, F.J.; German, D.P.; Herrel, A. Proximate drivers of population-level lizard gut microbial diversity: Impacts of diet, insularity, and local environment. Microorganisms 2020, 10, 1550. [Google Scholar] [CrossRef] [PubMed]
  52. Krajmalnik-Brown, R.; Ilhan, Z.E.; Kang, D.W.; Dibaise, J.K. Effects of gut microbes on nutrient absorption and energy regulation. Nutr. Clin. Pract. 2012, 27, 201–214. [Google Scholar] [CrossRef] [PubMed]
  53. Semova, I.; Carten, J.D.; Stombaugh, J.; Mackey, L.C.; Knight, R.; Farber, S.A.; Rawls, J.F. Microbiota regulate intestinal absorption and metabolism of fatty acids in the zebrafish. Cell Host Microbe. 2012, 12, 277–288. [Google Scholar] [CrossRef]
  54. Turnbaugh, P.J.; Ley, R.E.; Mahowald, M.A.; Magrini, V.; Mardis, E.R.; Gordon, J.I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006, 444, 1027–1031. [Google Scholar] [CrossRef]
  55. den Besten, G.; van Eunen, K.; Groen, A.K.; Venema, K.; Reijngoud, D.J.; Bakker, B.M. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lipid Res. 2013, 54, 2325–2340. [Google Scholar] [CrossRef] [PubMed]
  56. Sepulveda, J.; Moeller, A.H. The effects of temperature on animal gut microbiomes. Front. Microbiol. 2020, 11, 384. [Google Scholar] [CrossRef] [PubMed]
  57. Huus, K.E.; Ley, R.E. Blowing hot and cold: Body temperature and the microbiome. mSystems 2021, 6, e00707-21. [Google Scholar] [CrossRef] [PubMed]
  58. Moeller, A.H.; Ivey, K.; Cornwall, M.B.; Herr, K.; Rede, J.; Taylor, E.N.; Gunderson, A.R. The lizard gut microbiome changes with temperature and is associated with heat tolerance. Appl. Environ. Microbiol. 2020, 86, e01181-20. [Google Scholar] [CrossRef] [PubMed]
  59. Zhu, X.; Chen, J.; Du, Y.; Lin, C.; Qu, Y.; Lin, L.; Ji, X. Microbial communities are thermally more sensitive in warm-climate lizards compared with their cold-climate counterparts. Front. Microbiol. 2024, 15, 1374209. [Google Scholar] [CrossRef]
  60. Zhang, Z.; Zhu, Q.; Chen, J.; Khattak, R.H.; Li, Z.; Teng, L.; Liu, Z. Insights into the composition of gut microbiota in response to environmental temperature: The case of the Mongolia racerunner (Eremias argus). Glob. Ecol. Conserv. 2022, 36, e02125. [Google Scholar] [CrossRef]
  61. Lin, Z.; He, M.; Zhong, C.; Li, Y.; Tang, S.; Kang, X.; Wu, Z. Responses of gut microbiota in crocodile lizards (Shinisaurus crocodilurus) to changes in temperature. Front. Microbiol. 2023, 14, 1263917. [Google Scholar] [CrossRef]
  62. Sonnenburg, J.L.; Xu, J.; Leip, D.D.; Chen, C.H.; Westover, B.P.; Weatherford, J.; Buhler, J.D.; Gordon, J.I. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 2005, 307, 1955–1959. [Google Scholar] [CrossRef]
  63. Zhang, L.; Li, J.; Yun, T.; Qi, W.; Liang, X.; Wang, Y.; Li, A. Effects of pre-encapsulated and pro-encapsulated Enterococcus faecalis on growth performance, blood characteristics, and cecal microflora in broiler chickens. Poult. Sci. 2015, 94, 2821–2830. [Google Scholar] [CrossRef]
  64. Ley, R.E.; Bäckhed, F.; Turnbaugh, P.; Lozupone, C.A.; Knight, R.D.; Gordon, J.I. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA 2005, 102, 11070–11075. [Google Scholar] [CrossRef]
  65. Tong, Q.; Hu, Z.; Du, X.; Bie, J.; Wang, H. Effects of seasonal hibernation on the similarities between the skin microbiota and gut microbiota of an amphibian (Rana dybowskii). Microb. Ecol. 2020, 79, 898–909. [Google Scholar] [CrossRef]
  66. Derrien, M.; Vaughan, E.E.; Plugge, C.M.; de Vos, W.M. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 2004, 54, 1469–1476. [Google Scholar] [CrossRef]
  67. Derrien, M.; Van Baarlen, P.; Hooiveld, G.; Norin, E.; Müller, M.; de Vos, W.M. Modulation of mucosal immune response, tolerance, and proliferation in mice colonized by the mucin-degrader Akkermansia muciniphila. Front. Microbiol. 2011, 2, 166. [Google Scholar] [CrossRef]
  68. Li, G.; Li, J.; Kohl, K.D.; Yin, B.; Wei, W.; Wan, X.; Zhu, B.; Zhang, Z. Dietary shifts influenced by livestock grazing shape the gut microbiota composition and co-occurrence networks in a local rodent species. J. Anim. Ecol. 2019, 88, 302–314. [Google Scholar] [CrossRef]
  69. Tang, X.; Zhang, L.; Ren, S.; Zhao, Y.; Liu, K.; Zhang, Y. Stochastic processes derive gut fungi community assembly of plateau pikas (Ochotona curzoniae) along altitudinal gradients across warm and cold Seasons. J. Fungi 2023, 9, 1032. [Google Scholar] [CrossRef]
  70. Faust, K.; Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 2012, 10, 538–550. [Google Scholar] [CrossRef] [PubMed]
  71. Hernandez, D.J.; David, A.S.; Menges, E.S.; Searcy, C.A.; Afkhami, M.E. Environmental stress destabilizes microbial networks. ISME J. 2021, 15, 1722–1734. [Google Scholar] [CrossRef]
  72. Piccardi, P.; Vessman, B.; Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl. Acad. Sci. USA 2019, 116, 15979–15984. [Google Scholar] [CrossRef]
  73. Yang, D.; Kato, H.; Kawatsu, K.; Osada, Y.; Azuma, T.; Nagata, Y.; Kondoh, M. Reconstruction of a soil microbial network induced by stress temperature. Microbiol. Spectr. 2022, 10, e0274822. [Google Scholar] [CrossRef] [PubMed]
  74. Männistö, M.; Ganzert, L.; Tiirola, M.; Häggblom, M.M.; Stark, S. Do shifts in life strategies explain microbial community responses to increasing nitrogen in tundra soil? Soil Biol. Biochem. 2016, 96, 216–228. [Google Scholar] [CrossRef]
  75. Michalet, R.; Brooker, R.W.; Cavieres, L.A.; Kikvidze, Z.; Lortie, C.J.; Pugnaire, F.I.; Valiente-Banuet, A.; Callaway, R.M. Do biotic interactions shape both sides of the humped-back model of species richness in plant communities? Ecol. Lett. 2006, 9, 767–773. [Google Scholar] [CrossRef]
  76. Coyte, K.Z.; Schluter, J.; Foster, K.R. The ecology of the microbiome: Networks, competition, and stability. Science 2015, 350, 663–666. [Google Scholar] [CrossRef]
  77. Neutel, A.M.; Heesterbeek, J.A.P.; de Ruiter, P.C. Stability in real food webs: Weak links in long loops. Science 2002, 296, 1120–1123. [Google Scholar] [CrossRef]
  78. Rooney, N.; McCann, K.; Gellner, G.; Moore, J.C. Structural asymmetry and the stability of diverse food webs. Nature 2006, 442, 265–269. [Google Scholar] [CrossRef]
Microorganisms 12 01974 g001
Figure 1. Results of principal coordinates analysis (PCoA) on gut microbiota (A) and mean values (+SE) for distance to centroid (B) in a combination of two host species (E. multifasciata and L. reevesii) × two seasons (summer and winter). Each color in the figure represents a species × season combination (See the explanation on the right side of Plot B).
Figure 1. Results of principal coordinates analysis (PCoA) on gut microbiota (A) and mean values (+SE) for distance to centroid (B) in a combination of two host species (E. multifasciata and L. reevesii) × two seasons (summer and winter). Each color in the figure represents a species × season combination (See the explanation on the right side of Plot B).
Microorganisms 12 01974 g001
Microorganisms 12 01974 g002
Figure 2. The relative abundance of gut microbiota at the phylum and family levels in a combination of two host species × two seasons (A), the three phyla and 12 families of gut microbiota identified by LEfSe that differed significantly in relative abundance among the four combinations (B), and heatmap showing the relative abundance of each significantly differentially abundant phylum or family (C).
Figure 2. The relative abundance of gut microbiota at the phylum and family levels in a combination of two host species × two seasons (A), the three phyla and 12 families of gut microbiota identified by LEfSe that differed significantly in relative abundance among the four combinations (B), and heatmap showing the relative abundance of each significantly differentially abundant phylum or family (C).
Microorganisms 12 01974 g002
Microorganisms 12 01974 g003
Figure 3. The bacterial phyla and families that differed significantly in relative abundance between summer and winter in each lizard species.
Figure 3. The bacterial phyla and families that differed significantly in relative abundance between summer and winter in each lizard species.
Microorganisms 12 01974 g003
Microorganisms 12 01974 g004
Figure 4. Manhattan plots showing the ASVs that differed significantly in relative abundance between summer and winter. Each triangle or dot represents a single ASV colored by phylum. The dashed line shows the significance threshold (adjusted p < 0.05).
Figure 4. Manhattan plots showing the ASVs that differed significantly in relative abundance between summer and winter. Each triangle or dot represents a single ASV colored by phylum. The dashed line shows the significance threshold (adjusted p < 0.05).
Microorganisms 12 01974 g004
Microorganisms 12 01974 g005
Figure 5. Random forest model detects biomarker taxa that are most associated with summer and winter individuals of L. reevesii. (A) 10-fold cross-validation error as a function of the number of family or ASV used to differentiate gut microbiota between summer and winter (A), and the top three families and 25 ASVs identified by a random forest model (B). Biomarker taxa are ranked in descending order of their importance to the accuracy of the model.
Figure 5. Random forest model detects biomarker taxa that are most associated with summer and winter individuals of L. reevesii. (A) 10-fold cross-validation error as a function of the number of family or ASV used to differentiate gut microbiota between summer and winter (A), and the top three families and 25 ASVs identified by a random forest model (B). Biomarker taxa are ranked in descending order of their importance to the accuracy of the model.
Microorganisms 12 01974 g005
Microorganisms 12 01974 g006
Figure 6. Co-occurrence networks of ASVs with total relative abundances > 0.1% and present in at least three samples in each of the four host species × season combinations. Nodes represent ASVs, which are colored by phylum and sized by relative abundance. Links between nodes represent a significant correlation (|ρ| > 0.7, p < 0.01) between two ASVs, which are colored by direction (red: positive; grey: negative).
Figure 6. Co-occurrence networks of ASVs with total relative abundances > 0.1% and present in at least three samples in each of the four host species × season combinations. Nodes represent ASVs, which are colored by phylum and sized by relative abundance. Links between nodes represent a significant correlation (|ρ| > 0.7, p < 0.01) between two ASVs, which are colored by direction (red: positive; grey: negative).
Microorganisms 12 01974 g006
Table 1. Descriptive statistics, expressed as mean ± SE (range), for alpha diversity indexes of gut microbiota in summer (S) and winter (W) in two host species of warm-climate lizards, E. multifasciata (E) and L. reevesii (L). F values of two-way ANOVA with host species and season as the fixed factors and significance levels are given in the table.
Table 1. Descriptive statistics, expressed as mean ± SE (range), for alpha diversity indexes of gut microbiota in summer (S) and winter (W) in two host species of warm-climate lizards, E. multifasciata (E) and L. reevesii (L). F values of two-way ANOVA with host species and season as the fixed factors and significance levels are given in the table.
Observed ASVsFaith’s PDShannonEvenness
SummerWinterSummerWinterSummerWinterSummerWinter
E. multifasciata395.6 ± 28.2
(272–496)		353.0 ± 25.5
(273–515)		28.0 ± 1.2
(23.3–33.2)		26.6 ± 1.2
(21.6–33.6)		6.2 ± 0.3
(5.0–7.1)		5.9 ± 0.2
(4.8–7.1)		0.72 ± 0.02
(0.60–0.79)		0.70 ± 0.02
(0.57–0.79)
L. reevesii316.1 ± 16.1
(230–426)		235.6 ± 22.9
(142–348)		28.5 ± 1.1
(23.2–36.4)		23.8 ± 1.7
(17.0–32.5)		5.5 ± 0.2
(3.8–6.4)		4.0 ± 0.4
(1.9–5.8)		0.67 ± 0.02
(0.48–0.75)		0.50 ± 0.04
(0.26–0.69)
Host species (H)F1, 35 = 18.04, p < 0.01;
E > L		F1, 35 = 0.74, p = 0.40F1, 35 = 21.51 p < 0.01; E > LF1, 35 = 18.46, p < 0.01; E > L
Season (S)F1, 35 = 7.05, p = 0.01;
S > W		F1, 35 = 4.98, p = 0.03;
S > W		F1, 35 = 10.98, p < 0.01; S > WF1, 35 = 10.24, p < 0.01; S > W
H × S interactionF1, 35 = 0.67, p = 0.42F1, 35 = 1.42, p = 0.24F1, 35 = 5.09, p = 0.03F1, 35 = 5.99, p = 0.02
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhu, X.; Jiang, N.; Mai, T.; Wu, S.; Yao, Y.; Du, Y.; Lin, C.; Lin, L.; Ji, X. Gut Microbial Communities Are Seasonally Variable in Warm-Climate Lizards Hibernating in the Winter Months. Microorganisms 2024, 12, 1974. https://doi.org/10.3390/microorganisms12101974

AMA Style

Zhu X, Jiang N, Mai T, Wu S, Yao Y, Du Y, Lin C, Lin L, Ji X. Gut Microbial Communities Are Seasonally Variable in Warm-Climate Lizards Hibernating in the Winter Months. Microorganisms. 2024; 12(10):1974. https://doi.org/10.3390/microorganisms12101974

Chicago/Turabian Style

Zhu, Xiaming, Neng Jiang, Tingye Mai, Shulin Wu, Yuntao Yao, Yu Du, Chixian Lin, Longhui Lin, and Xiang Ji. 2024. "Gut Microbial Communities Are Seasonally Variable in Warm-Climate Lizards Hibernating in the Winter Months" Microorganisms 12, no. 10: 1974. https://doi.org/10.3390/microorganisms12101974

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop