Submit or Track your Manuscript LOG-IN

Changes in the Intestinal Microbes of Wild Spermophilus dauricus (Rodentia: Sciuridae) in Different Periods of Hibernation (A Model of Gut Ischemic Reperfusion Injury)


Changes in the Intestinal Microbes of Wild Spermophilus dauricus (Rodentia: Sciuridae) in Different Periods of Hibernation (A Model of Gut Ischemic Reperfusion Injury)

Chun Shi, Juanjuan Guo, Meng Li, Qi Yang and Jingang Li*

College of Life Science, Shaanxi Normal University, Xi’an 710000, China.


Reperfusion injury after intestinal ischemia and hypoxia is a complex and comprehensive disease with a poor prognosis and high fatality rate. However, whether it can promote or slow down intestinal ischemia-reperfusion injury remains unclear. Spermophilus dauricus is a hibernation animal with a short awake time that interrupts the long hibernation period. When these events occurred, the long-term ischemic intestine in hibernation rapidly congested and entered the hibernation and ischemic state again. However, Spermophilus dauricus is not sick, and this might be a good model for gut ischemic reperfusion injury. In this animal research study, 16s rRNA gene sequencing was used to detect the bacterial community composition of S. dauricus intestine before, during, and after the artificial hibernation process and short awakening hibernation, respectively. The results showed that there was no significant alpha diversity during the four periods; however, the beta diversity was different, especially in the awakening period. These differences in beta diversity may be due to the significantly increased abundance of 11 kinds of bacteria, including Anoxybacillus, Geobacillus, Klebsiella, Thermus, and Acinetobacter (P<0.05), and the significantly reduced abundance of 16 species of bacteria, including Acinetobacter, Aquabacterium, Acidovorax, Thermus, and Klebsiella (P<0.05) in the awakening period. This study provides guidance and reference for the exploration of animal hibernation mechanisms and research on intestinal ischemic re-enema injury.

Article Information

Received 31 May 2022

Revised 23 June 2022

Accepted 09 July 2022

Available online 14 November 2022

(early access)

Published 10 April 2023

Authors’ Contribution

Conceptualization, CS and JL. Methodology, CS. Formal analysis, CS and JG. Investigation, CS, ML and QY. Resources CS. Data curation, CS. Writing original draft preparation, CS. Writing review and editing, CS. Supervision, CS. Project administration, CS and JL. Funding acquisition, CS and JL. All authors have read and agreed to the published version of the manuscript.

Key words

Hibernation, Awakening period of hibernation, Gut microbes, Intestinal ischemic re-enema injury, 16s rDNA


* Corresponding author:

0030-9923/2023/0003-1353 $ 9.00/0

Copyright 2023 by the authors. Licensee Zoological Society of Pakistan.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


Ischemic injury is an organic injury to the organs of body in a state of ischemia and hypoxia (Hummitzsch et al., 2019). After the tissue cells have suffered ischemia for a certain period of time, blood flow is restored and the degree of tissue damage increases sharply. Reperfusion injury after intestinal ischemia and hypoxia is a complex and comprehensive disease with a poor prognosis and high fatality rate. It is important to study the mechanism of intestinal ischemia-reperfusion injury to prevent intestinal injury (Ikebuaso et al., 2011).

In recent years, an increasing number of studies have revealed that gut microbes play an important regulatory role, and they can perform a function as the second gene pool in organisms (Barko et al., 2017). Bile acids produced by intestinal microbes can regulate intestinal regulatory T cell population and affect immunity (Liu et al., 2020). The positive or negative correlation between the intestinal microbial community and insulin resistance can affect plasma metabolism and immune performance of the body (Fujisaka et al., 2018). Intestinal microbes can regulate glucose homeostasis and energy balance via the intestinal-brain axis (Kim et al., 2018; Bauer et al., 2018). Moreover, the composition of fungal flora can slow down the injury of intestinal ischemia and reperfusion (Papoff et al., 2012). Colonic mucus in the intestine can cause intestinal bacteria to interact with colon cells, which is related to inflammation. Excretion can promote the recovery of mucus after intestinal ischemia and slow reperfusion (Grootjans et al., 2013). Short-chain fatty acids produced by microorganisms in the intestine can inhibit histone deacetylase activity and expression during chromosome reorganization, thereby improving the intestinal tract ischemic reperfusion injury (Andrade-Oliveira et al., 2015). In surgical liver surgery, local congestion of the intestine and colonization of bacteria in the portal vein of the intestine or through the intestine-liver axis are regulated via the lipopolysaccharide (LPS)-toll-like receptor 4 (TLR4) pathway. TLR4 in the liver causes cancer cell regeneration (Orci et al., 2018). Studies have shown that symbiosis of intestinal microbes can regulate the intestinal barrier through pregnane X receptor (PXR) and TLR4 (Madhukumar et al., 2014). They can also affect TLR2, TLR4, and its adaptor protein, myeloid differentiation primary-response 88 (MyD88), oxidative stress, nitric oxide, or the formation of intestinal ischemic reperfusion injury (Yuji et al., 2018). However, whether changes in the composition of intestinal bacterial flora and metabolites have a direct regulatory effect on intestinal ischemic reperfusion injury and whether they can promote or slow down intestinal ischemic reperfusion injury remains elusive.

This study used wild S. dauricus during hibernation as a research model. Spermophilus dauricus is a hibernation animal with a short awake time that interrupts the long hibernation period. When these events occur, the long-term ischemic intestine in hibernation rapidly congests and the animal enters the hibernation and ischemic state again. However, as S. dauricus is not sick, it might be a good model for gut ischemic reperfusion injury. During hibernation, the basal metabolism of S. dauricus is extremely low and the intestine is in a certain ischemic state. However, during the short awakening period, the physiological metabolism of S. dauricus rapidly recovers and blood reperfusion occurs. In this study, we selected the pre-hibernation, hibernation, post-hibernation, and awakening periods in the hibernation process as time nodes to explore the hibernation and awakening periods of S. dauricus (intestinal ischemic reperfusion period) microbial composition. We analyzed the physiological mechanism of S. dauricus against intestinal ischemic reperfusion from the perspective of microorganisms, studied the possible microbial regulation mechanisms related to the formation of intestinal ischemic reperfusion, and provided references for future research on intestinal ischemia-reperfusion injury.

Materials and Methods

Animal management

All animal procedures (care, handling, and experimentation) followed the guidelines established by the rules for the administration of affairs concerning experimental animals. For studying gut microbes, 60 wild Spermophilus dauricus were divided into four groups, pre-hibernation (PRE, n= 15), middle hibernation group (HIB, n= 15, after 60 days of hibernation), intervene arousal group (IBA, n=15), and post-sleep group (POST, n=15, 90±12 days after hibernation, 2 days after hibernation). Temperature and light were artificially controlled to simulate winter to facilitate natural entry of these species into the hibernation state. At the same time, the weather conditions were controlled to make S. dauricus appear in an arousal state according to their habits. Finally, three S. dauricus were randomly selected from each group for collection of intestinal feces because S. dauricus is a nationally protected animal in China. The digestive tract of the hibernating group was in a state of ischemia whereas that of the arousal group was in a state of ischemia reperfusion.

DNA extraction, PCR, and sequencing

The total genomic DNA from samples was extracted using the E.Z.N.A.® fece DNA kit (Omega Bio-tek, Norcross, GA, USA) according to manufacturer’s protocol. The primer set 515F-GTGCCAGCMGCCGCGGTAA-806R-GGACTACHVGGGTWTCTAAT was used for 16S rRNA gene amplification with specific barcodes. All PCR reactions were performed in 30 μL volume with 15 μL of Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 μM of forward and reverse primers, and approximately 10 ng template DNA. Thermal cycling consisted of initial denaturation at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, elongation at 72℃ for 60 s, and finally, elongation at 72℃ for 5 min. Products mixed at equidensity ratios were purified using the GeneJET Gel Extraction Kit (Thermo Fisher Scientific). All library amplifications were generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA). Finally, the library was sequenced on an Illumina MiSeq platform, and 300 bp paired-end reads were generated at BGI Tech Co., Ltd. (Shenzhen, China).

Sequence denoising, OTU (operational taxonomic units) clustering, diversity analysis, and statistics

Raw reads were combined, trimmed, and dereplicated using Vsearch (v2.17.0) (Rognes et al., 2016) to remove low-quality and mismatched reads. The high-quality reads were clustered into OTUs using Usearch v10.0.240 (Edgar and Valencia, 2018; Edgar, 2010). Reference-based chimeras were detected and removed using Vsearch, based on RDP_6s_V16_sp ( Following this, the feature table was prepared, and plastids and non-bacteria were removed using Vsearch (cutoff= 0.6). The lowest sequence number across all samples was used to rarefy each sample and equalize the sequencing depth. The rarefied sequences were calculated for alpha and beta diversity indices using Usearch. The OTUs were annotated using GreenGene (v13.8, OTUs with a selected relative abundance of more than 0.2% were aligned using MUSCLE (Robert, 2004) to build a cladogram in iQtree (Lam-Tung et al., 2015). The cladogram was visualized using table2itol ( and iTOL ( Biomarkers were predicted using LEfSe (Segata et al., 2011) ( Finally, differences in bacterial abundance were analyzed using STAMP (v2.1.3,, Parks et al., 2014). The two groups were compared using Welch’s t-test, and P-value was adjusted using Benjamin-Hochberg FDR.

The data that support the study findings have been deposited into the CNGB Sequence Archive (CNSA) of the China National GeneBank BataBase (CNGBdb) with accession number CNP0002091.


Sample sequencing data statistics

The results showed that more than 31.34 Mbp raw data was present in each sample. The raw reads were greater than 63373 × 2. After cutting the barcode and primers, the availability of clean data was found to be greater than 95.16% in each sample. All tag numbers exceeded 47502 (Table I).

As the sequencing depth increased, the number of OTUs obtained from the sample saturated when it was above 20% in all groups (Fig. 1). Based on de-redundancy, bacteria in HIB, IBA, POST, and PRE groups clustered into 44, 17, 43, and 65 effective OTUs, respectively. Three identical OTUs were present in the four groups and no unique OTUs were present in the IBA group; all OTUs were identical in the other three groups and only the abundance was different (Fig. 2).


Alpha and beta diversities

After diversity analysis, no significant difference was observed in ACE, chao1, in vsimpson, richness, Shannon, and Simpson alpha diversity indices between each group (Fig. 3). No significant difference was observed in the abundance and type of bacteria in each group, and the distribution of bacteria in each group was uniform.


Table I. Sample sequencing data statistics.

Sample name

Reads length (bp)

Raw data (Mbp)

Adap-ter (%)

N base (%)

Ploy base (%)

Low quality (%)

Clean data (Mbp)

Data utili-zation ratio (%)

Raw reads

Clean reads

Read utili-zation ratio (%)

Tag number
































































































































































The Bray-Curtis distance in each group was calculated. We used unconstrained (principal coordinate analysis, PCoA) and constrained ordinations (constrained PCoA, cPCoA) to show the Bray-Curtis distance in all groups (Fig. 4). The results indicated that HIB and POST groups had very close Bray-Curtis distances whereas in IBA or PRE groups, and the distances were far away. To demonstrate details in the Bray-Curtis distance, we created a heatmap of the Bray-Curtis distance between each sample (Fig. 5). This clearly depicted the Bray-Curtis distance between each sample. A high similarity was observed in the beta diversities between HIB and POST groups. However, the diversities were different from those in IBA and PRE groups. The Bray-Curtis distance of beta diversity was very close in HIB and POST groups, followed by PRE group.



Bacterial community distribution at the phylum level

We counted relative abundance of the Top 7 bacteria in each group at the phylum level (Fig. 6). Before hibernation (PRE group), in S. dauricus intestine, Firmicutes was the dominant bacteria, with the highest abundance, followed by verrucomicrobia, bacteroidetes, actinobacteria, and proteobacteria. During the hibernation (HIB group) and post-hibernation (POST group) periods, intestinal bacteria of S. dauricus were similarly distributed at the phylum level, and the proteobacteria phylum was the dominant bacteria group, followed by deinococcus-thermus, firmicutes, bacteroidetes, fusobacteria, and others. During the awakening period (IBA group, intestinal ischemic reperfusion period), Firmicutes was the dominant bacterial group followed by proteobacteria, and the relative abundances of bacteroidetes, deinococcy-thermus, verrucomicrobia, and other phyla were very low in the intestine of S. dauricus. It was observed that the phylum intestinal bacterial composition of S. dauricus significantly changed during the hibernation period compared with the pre-hibernation period. The bacterial abundance of proteobacteria and deinococcus-thermus phylum increased while that of firmicutes and Verrucomicrobia decreased. In the awakening period (IBA group), the composition of the bacterial community changed more drastically and the relative abundance of Firmicutes and Proteobacteria increased sharply while that of other bacteria decreased. Additionally, in the POST group (2 days after the end of hibernation), the composition of the intestinal bacterial phyla of S. dauricus was similar to that in the hibernation period, and it did not recover in the pre-hibernation period. It might be expected that it would take some time for the bacterial composition to adjust and recover.



Bacterial community distribution at the genus level

Similarly, we counted the relative abundance of the Top 7 bacteria in each group at the genus level to explore the changes (Fig. 7). In the PRE group, Lactobacillus was the dominant genus with the highest intestinal abundance. The HIB group also had a similar bacterial construction at the genus level as that in the POST group; Klebsiella was the predominant bacteria, followed by Acinetobacter, Thermus, and Pseudomonas. However, in the IBA group, Anoxybacillus was the dominant genus, followed by Geobacillus and Klebsiella, and the relative abundances of other bacteria were low. Compared to the pre-hibernation period, intestinal bacteria at the genus level significantly changed during the hibernation period. The abundance of Lactobacillus decreased sharply while the abundance of Klebsiella, Acinetobacter, Thermus, Pseudomonas, and Anoxybacillus increased sharply. Particularly, during the awakening period (IBA group), the abundance of Lactobacillus decreased sharply, and the abundance of Anoxybacillus and Geobacillus increased sharply, making them the main bacteria. Similarly, in the POST group, the composition of intestinal bacteria was still similar to that in the hibernation period.

The main OTU evolutionary tree of intestinal bacteria

We constructed a phylogenetic tree for the top 100 OTUs with relative abundances above 0.2% and performed relative annotations. The results were shown in Figure 8. It showed the expression of the top 100 OTUs in each group and/or sample with the class phylum to family. Among the top 100 OTUs, Firmicutes had the highest abundance, followed by Proteobacteria, and the rest were Bacteroidetes and Actinobacteria.



LEfSe analysis for biomarkers

To predict the possible differential biomarker bacteria of the intestinal bacteria of S. dauricus in different hibernation periods, LEfSe analysis was conducted, and the results are shown in Figure 9. HIB and POST groups had 26 (p_Proteobacteria, g_Klebsiella, o_Enterobacteriales, etc.) and 20 (c_Gammaproteobacteria, o_Pseudomonadales, f_Pseudomonadaceae, etc.) differential biomarkers, respectively. PRE group had seven biomarkers, namely f_Lachnospiraceae, p_Actinobacteria, f_Corynebacteriaceae, g_Corynebacterium, o_Actinomycetales, c_Actinobacteria, and one g_ unassigned. The IBA group contained only four biomarkers, o_Bacillales, f_Bacillaceae_1, p_Firmicutes, g_Anoxybacillus, and g_Geobacillus.

Comparison of the main differences in OTU and bacterial abundance among IBA group and PRE and HIB groups

Previous beta diversity results had suggested that the bacterial compositions of HIB and POST groups were relatively similar. Therefore, the HIB group was used for comparison with other groups. OTUs play a key role in IBA, PRE, and HIB groups, the significant OTU abundance and its corresponding bacteria were analyzed between multiple groups. As shown in Figure 10A, compared to the bacteria in PRE group, the bacteria in IBA group had a higher abundance of 16 OTUs, such as OTU7, OTU49, and OTU27, and lower abundance of 12 OTUs, including OTU45, OTU54, and OTU18. Similarly, as shown in Figure 10B, compared to the PRE group, the bacteria in IBA group had a lower abundance of 20 OTUs such as OTU10, OTU11, and OTU2 and higher abundance of four OTUs, such as OTU3, OTU4, OTU1, OTU55, and OTU31.

After annotating these OTUs as the corresponding bacteria, we compared the abundance of the main bacteria in IBA group with that in PRE and HIB groups at the phylum (Fig. 11) and genus levels (Fig. 12). We counted the bacteria with significant differences in abundance.


At the phylum level (Fig. 13), the bacterial abundance of Actinobacteria in PRE group was significantly higher than that in HIB, IBA, and POST groups (P<0.05). The bacterial abundance of Deinococcus-Thermus in POST group was significantly higher than that in PRE and IBA groups (P<0.05), and its bacterial abundance was significantly


higher in HIB than that in PRE and IBA groups (P<0.05). The abundance of Firmicutes bacteria in POST group was significantly lower than that in PRE and IBA groups (P<0.05); however, it was significantly lower in HIB group as compared to that in PRE and IBA groups (P<0.05). The abundance of Proteobacteria was significantly lower in PRE group than that in POST and IBA groups (P<0.05); however, its abundance was significantly higher in POST group than that in IBA group (P<0.05). Moreover, its abundance was significantly higher in HIB group than that in PRE and IBA groups (P<0.05).


At the genus level (Fig. 14), compared to the abundance in PRE group, the abundance of 11 kinds of bacteria, including Anoxycacillus, Geobacillus, Klebsiella, Thermus, and Acinetobacter, in IBA group significantly increased (P<0.05). Meanwhile, compared to the abundance in HIB group, the abundance of Anoxybacillus and Geobacillus in IBA group significantly increased (P<0.05). However, the abundance of 16 species of bacteria, including Acinetobacter, Aquabacterium, Acidovorax, Thermus, and Klebsiella, significantly decreased (P<0.05). Compared to the abundance in PRE group, the abundance of Klebsiella, Pseudomonas, Acinetobacter, Thermus, and Acidovorax in HIB group significantly increased (P<0.05).




In this study, we found that fewer OTUs were shared between the pre-hibernation and other periods of hibernation as compared to common OTUs in each period of hibernation. Additionally, although the beta diversity distance of S. dauricus gut microbes was obvious, no significant difference was observed in the alpha diversity at each period. The reason for this remains unclear and further research is required.

Hibernation is an adaptation of hibernators. It can alter the intestinal epithelial, immune, and cell survival pathways (Kurtz et al., 2021). Under hibernation fasting and cold conditions, the intestinal microbial community adaptively reorganizes to maintain physiological functions, and the intestinal microbiota abundance, composition, and diversity may change. Therefore, studies have analyzed changes in the intestinal microbial community of hibernating animals during hibernation to provide a new perspective for the treatment of intestinal diseases, including ischemia/reperfusion injury (Sisa et al., 2017). Since no food is supplied during hibernation, it is foreseeable that the gut microbial community of hibernating animals will change during different hibernation periods. In this study, prior to hibernation, Firmicutes was the dominant bacteria in the gut of S. dauricus, and its abundance decreased during hibernation; however, Bacteroidetes showed an opposite trend. The results were similar to those of the bacterial communities in hibernating ground squirrels and brown bears (Dill-McFarland et al., 2015; Carey et al., 2013; Sommer et al., 2016). Compared to before hibernation, during post hibernation, the bacterial community composition of Bombus terrestris intestine was higher and abundance of the core bacteria Gilliamella (Proteobacteria, Orbaceae) and Snodgrassella (Proteobacteria, Neisseriaceae) decreased but that of other non-core bacteria increased (Bosmans et al., 2018). Similarly, in this study, Geobacillus was also the core bacteria, especially the second most dominant community in the awakening period in IBA group. However, compared to the bacteria before and during hibernation, the Geobacillus of S. dauricus intestine increased during the awakening period. These changes might cause hibernation to affect lipid metabolism, such as triglyceride, cholesterol, and bile acid metabolism (Sommer et al., 2016). Further, it may convert the metabolic function of intestinal microbes from carbohydrate-related to lipid-related functional categories (Xiao et al., 2019) and change the microbial short-chain fatty acids and other metabolite features (Carey and Assadi, 2017).

The hibernators had core OTUs that were rarely distributed among non-hibernating animals, and hibernation might reduce the microbial diversity in frogs (Weng et al., 2016).


This study demonstrated changes in the diversity of intestinal microbial communities before, during, and after the hibernation and awakening periods. The results showed that there was no significant change in alpha diversity during the four periods; however, the beta diversity was different, especially in the awakening period. The differences in beta diversity may be due to the fact that hibernation can increase the abundance of four species of bacteria, such as Klebsiella, in the intestine of Spermophilus dauricus, as compared to before hibernation. The arousal (ischemic reperfusion) phase may lead to an increase in bacterial abundance in the intestine, such as that of Anoxybacillus. Compared to hibernation, the awakening (ischemic reperfusion) phase leads to an increase in the abundance of Anoxybacillus and Geobacillus and a decrease in the abundance of 16 species of bacteria, including Acinetobacter, in the intestines of S. dauricus. Although we know that the intestinal bacterial community is closely related to animal behavior and physiological metabolism, this study also found that the intestinal community significantly changes during hibernation; however, the specific interaction mechanism is still unclear. Nevertheless, this study provides guidance and reference for the exploration of animal hibernation mechanisms and research on intestinal ischemic re-enema injury.


This work was funded by the Natural Science Foundation of Shaanxi Province, China (No. 2020JM-300)

Institutional review board statement

Ethical review and approval were waived for this study, due collection samples from intestinal feces, which was a non-invasion method.

Data availability statement

Not applicable.

Statement of conflict of interest

The authors have declared no conflict of interest.


Andrade-Oliveira, V., Amano, MT., Correa-Costa, M., Castoldi, A., Felizardo, R.J.F., Almeida, D.C.D., Bassi, E.J., Moraes-Vieira, P.M., Hiyane, M.I., Rodas, A.C.D., Peron, J.P.S., Aguiar, C.F., Reis, M.A., Ribeiro, W.R., Valduga, C.J., Curi, R., Vinolo, M.A.R., Ferreira, C.M., and Câmara, N.O.S., 2015. Gut bacteria products prevent AKI induced by ischemia-reperfusion. J. Am. Soc. Nephrol., 26: 1877–1888.

Barko, P.C., Mcmichael, M.A., Swanson, K.S., and Williams, D.A., 2017. The gastrointestinal microbiome: A review. J. Vet. Int. Med., 32: 9–25.

Bauer, P.V., Frank, A.D., Waise, T.M.Z., Dranse, H.J., Rasmussen, B.A., Puri, A., Rasti, M., O`Brien, C.A., and Lam, T.K.T., 2018. Lactobacillus gasseri in the upper small intestine impacts an ACSL3-dependent fatty acid-sensing pathway regulating whole-body glucose homeostasis. Cell Met., 27: 572–587.

Bosmans, L., Pozo, M.I., Verreth, C., Crauwels, S., Wäckers, F., Jacquemyn, H., and Lievens, B., 2018. Hibernation leads to altered gut communities in bumblebee queens (Bombus terrestris). Insects, 9: 188.

Carey, H.V., Assadi-Porter, F.M., 2017. The hibernator microbiome: Host-bacterial interactions in an extreme nutritional symbiosis. Annu. Rev. Nutr., 37: 477–500.

Carey, H.V., Walters, W.A., and Knight, R., 2013. Seasonal restructuring of the ground squirrel gut microbiota over the annual hibernation cycle. Am. J. Physiol. Reg. I, 304: 33–42.

Dill-McFarland, K.A., Neil, K.L., Zeng, A., Sprenger, R.J., Kurtz, C.C., Suen, G., and Carey, H.V., 2015. Hibernation alters the diversity and composition of mucosa-associated bacteria while enhancing antimicrobial defence in the gut of 13-lined ground squirrels. Mol. Ecol., 23: 4658–4669.

Edgar, R.C., 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26: 2460–2461.

Edgar, R.C., and Valencia, A., 2018. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics, 34: 2371–2375.

Fujisaka, S., Avila-Pacheco, J., Soto, M., Kostic, A., Dreyfuss, J.M., Pan, H., Ussar, S., Altindis, E., Ning, L., Bry, L., Clish, C.B., and Kahn, C.R., 2018. Diet, genetics, and the gut microbiome drive dynamic changes in plasma metabolites. Cell Rep., 22: 3072–3086.

Grootjans, J., Hundscheid, I.H.R., Lenaerts, K., Boonen, B., Benes, I.B., Vereyen, F.K., Dejong, C.H., Meyenfeldt, M.R., Beets, G.L., and Buurman, W.A., 2013. Ischaemia-induced mucus barrier loss and bacterial penetration are rapidly counteracted by increased goblet cell secretory activity in human and rat colon. Gut Microb., 62: 250–258.

Hummitzsch, L., Zitta, K., Berndt, R., Wong, Y.L., Rusch, R., Hess, K., Wedel, T., Gruenewald, M., Cremer, J., Steinfath, M., and Albrecht, M., 2019. Remote ischemic preconditioning attenuates intestinal mucosal damage: Insight from a rat model of ischemia-reperfusion injury. J. Transl. Med., 17: 136.

Ikebuaso, A.D., Yama, O.E., Amah, C.I., Oremosu, A.A., and Mojekwu, T.O., 2011. Palliative effect of pausinystallia macroceras on testicular ischemic reperfusion injury in wistar rats: A histological study. Int. J. Morphol., 29: 1256–1262.

Kim, N., Yun, M., Oh, Y.J., and Choi, H.J., 2018. Mind-altering with the gut: Modulation of the gut-brain axis with probiotics. J. Microbiol., 56: 172–182.

Kurtz, C.C., Otis, J.P., Regan, M.D., and Carey, H.V., 2021. How the gut and liver hibernate. Comp. Biochem. Physiol. A, 253: 110875.

Lam-Tung, N., Schmidt, H.A., Arndt, V.H., and Quang, M.B., 2015. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol., 1: 268–274.

Liu, T.Y., Song, X.L., Khan, S., Li, Y., Guo, Z.X., Li, C.Q., Wang, S., Dong, W.X., Liu, W.T., Wang, B.M., and Cao, H.L., 2020. The gut microbiota at the intersection of bile acids and intestinal carcinogenesis: An old story, yet mesmerizing. Int. J. Cancer, 146: 1780–1790.

Madhukumar, V., Mukherjee, S., Wang, H.W., Li, H., Sun, K., Benechet, A.P., Qiu, Z.J., Maher, L., Redinbo, M.R., Phillips, R.S., Fleet, J.C., Kortagere, S., Mukherjee, P., Fasano, A., Ven, J.L., Nicholson, J.K., Dumas, M.E., Khanna, K.M., and Mani, S., 2014. Symbiotic bacterial metabolites regulate gastrointestinal barrier function via the xenobiotic sensor PXR and Toll-like receptor 4. Immunity, 41: 296–310.

Orci, L.A., Lacotte, S., Delaune, V., Slits, F., Oldani, G., Lazarevic, V., Rossetti, C., Rubbia-Brandt, L., Morel, P., and Toso, C., 2018. Effects of the gut-liver axis on ischemia-mediated hepatocellular carcinoma recurrence in the mouse liver. J. Hepatol., 68: 978.

Papoff, P., Ceccarelli, G., d’Ettorre, G., Cerasaro, C., Caresta, E., Midulla, F., and Moretti, C., 2012. Gut microbial translocation in critically ill children and effects of supplementation with pre- and pro biotics. Int. J. Microbiol., 2012: 151393.

Parks, D.H., Tyson, G.W., Hugenholtz, P., and Beiko, R.G., 2014. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics, 30: 3123–3124.

Robert, C.E., 2004. Muscle: Multiple sequence alignment with high accuracy and high throughput. Nucl. Acids Res., 32: 1792–1797.

Rognes, T., Flouri, T., Nichols, B., Quince, C., and Mahé, F., 2016. Vsearch: A versatile open source tool for metagenomics. PeerJ, 4: 2584.

Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, WS., and Huttenhower, C., 2011. Metagenomic biomarker discovery and explanation. Genome Biol., 12: R60.

Sisa, C., Turroni, S., Amici, R., Brigidi, P., Candela, M., and Cerri, M., 2017. Potential role of the gut microbiota in synthetic torpor and therapeutic hypothermia. World J. Gastroenterol., 23: 406–413.

Sommer, F., Ståhlman, M., Ilkayeva, O., Arnemo, J.M., Kindberg, J., Josefsson, J., Newgard, C.B., Fröbert, O., and Bäckhed, F., 2016. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep., 14: 1655–1661.

Weng, F.C.H., Yang, Y.J., and Wang, D., 2016. Functional analysis for gut microbes of the brown tree frog (Polypedates megacephalus) in artificial hibernation. BMC Genom., 17: 102.

Xiao, G.H., Liu, S., Xiao, Y.H., Zhu, Y., Zhao, H.B., Li, A.Q., Li, Z.L., and Feng, J., 2019. Seasonal changes in gut microbiota diversity and composition in the greater horseshoe bat. Front. Microbiol., 10: 2247.

Yuji, N., Toshio, W., Sunao, S., Koji, O., Tetsuya, T., and Yasuhiro F., 2018. Microbiome and intestinal ischemia/ reperfusion injury. J. clin. Biochem. Nutr., 63: 26–32.

To share on other social networks, click on any share button. What are these?

Pakistan Journal of Zoology


Vol. 55, Iss. 3, Pages 1003-1500


Click here for more

Subscribe Today

Receive free updates on new articles, opportunities and benefits

Subscribe Unsubscribe