Multiple Low-Dose Radiation ameliorates type-2 diabetes mellitus via gut microbiota modulation to activate TLR4/MyD88/NF-κB pathway

doi:10.21203/rs.3.rs-4893469/v1

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Multiple Low-Dose Radiation ameliorates type-2 diabetes mellitus via gut microbiota modulation to activate TLR4/MyD88/NF-κB pathway

https://doi.org/10.21203/rs.3.rs-4893469/v1

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Background

Type 2 diabetes mellitus (T2DM) is the fastest-growing metabolic disease in the world. The gut microbiota is linked to the T2DM. Recent studies have showed that the metabolism of gut microbiota can trigger T2DM. Low dose Radiation (LDR) has been proved to activate various protective bioeffects on diabetes. However, the underlying mechanisms remain unclear.

Method

In this study, T2DM mouse model was established using high fat diet combined with streptozocin (STZ) injection, and then exposed to multiple 75 mGy LDR every other day for one month. The changes of blood glucose levels, body weight, organ weight and damage of pancreas were measured. In addition, 16S rDNA amplicon sequencing was used to detect gut microbiota alteration. Metabolic profiling was carried out using liquid mass spectrometry system, followed by the combinative analysis of gut microbiota alteration. Furthermore, inflammatory factors and related pathways were detected.

Results

We found that LDR attenuate blood glucose level and weight of body, pancreas, brain, liver and testis in T2DM mice, and reduce pancreas impairment. In addition, in the gut, LDR regulated the relative abundance of bacilli, desulfobacterota, verrucomicrobiota and proteobacteria. The non-target metabolomics analysis found that LDR significantly improve the metabolic abnormalities in T2DM, which is closely related to the gut microbiota abundance. Furthermore, the inflammatory effects activated by TLR4/MyD88/NF-κB pathways in T2DM were ameliorated by LDR. Conclussion: These results suggest that LDR may exert a beneficial role in T2DM by modulating gut microbiota and metabolites, especially in TLR4/MyD88/NF-κB pathway.

Type 2 diabetes mellitus

Low dose radiation

gut microbiota

metabolites

Toll like receptor 4

Diabetes is a common problem facing modern society, Type 2 diabetes mellitus (T2DM) accounts for more than 90% of the total number of diabetes patients, which is characterized by insulin resistance, a defect in the ability of insulin to absorb glucose, and patients often have metabolic abnormalities [1–3]. The factors and mechanisms that trigger T2DM have been intensely discussed, genetic factor, high caloric intake and physical inactivity is the major risk factor [4]. Insulin is produced by the β-cells of the pancreas, if the amount of insulin fails to compensate the physical demand, the person will develop hyperglycemia [5]. Poorly controlled diabetes and metabolic disorders associated with T2DM, such as impaired lipid, amino acids and bile acids, modulates insulin sensitivity. The etiologies of metabolic disorders are closely related with obesity resulted from high fat diet (HFD) consumption, energy expenditure, genetics, level of physical activity, etc. [6–8]. Therefore, it is very meaningful to further investigate the influencing factors of metabolic dysfunction.

In recent years, the gut microbiota, described as “the second genome of the body”, potentially affect obesity, energy metabolism, glucose metabolism and immunity [9–11]. Studies have shown that excessive intake of salt, sugar and fat will lead to over nutrition, and affect the diversity and stability of intestinal microbes causing chronic inflammation in the intestines and insulin resistance, ultimately lead to diabetes [12–14]. Many inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interferon γ (IFNγ), have reported to be elevated in the diabetic models [15]. These cytokines also play a rather important role through a positive feedback loop, primarily involving the nuclear factor-κB (NF-κB) activation [16]. In gut, the elevation of sugar metabolites will cause pathological increase of bacteria Enterobacter and other intestinal flora, resulting in inflammatory response and insulin resistance [17]. Meantime, the impaired permeability of the intestinal mucosa promote the immune inflammatory response, further dysregulate glucose levels, and contribute the occurrence and development of T2DM [18, 19]. Lipopolysaccharide (LPS) is a component of the outer wall of the cell wall of Gram-negative bacteria, it is a substance composed of lipids and polysaccharides. The translocation of bacterial LPS possibly leads to metabolic endotoxemia and insulin [20]. LPS forms a complex with glucose phosphate isomerase anchor protein CD14, which is covered by mononuclear giant TLR-4 recognition on the surface of phages promotes the development of inflammation and insulin resistance through TLRs signaling pathway [21]. Therefore, the interventions targeting microbiota might influence the glucose-lowing effects [22].

Previous studies have shown that low dose radiation (LDR) can activate anti-oxidative and anti-inflammatory effects [23]. LDR also has multiple beneficial effects on renal dysfunction, cardiac inflammation, brain injury and reproductive damage in diabetes [24–26]. Interestingly, LDR modified gut microbiota to increase the abundance of Clostridium, Helicobacter and Oscilibacter, while to decrease the abundance of Bacteroides and Barnesiella [27]. Additionally, several studies on murine and human models showed that HFD increased endotoxemia, which disrupted the epithelial barrier and enhanced permeation of luminal LPS [28–29]. Other studies demonstrated a significant increase in the Desulfovibrionaceae family in both obese human and mice compared to the lean individuals, which might influence pro-inflammatory through LPS [30, 31].

Over the years, although substantial efforts were made to elucidate the mechanisms of protective effects of LDR on T2DM, it still unclear. In this study, we investigated whether LDR exhibits protective property in diabetes. Additionally, we underlied the anti-inflammatory mechanisms resulted from gut microbiota and metabolites through TLR4/MyD88/NF-κB pathway after LDR. We showed that LDR suppresses the high glucose level, reduces organ weight, and change the abundance of bacilli and desulfobacterota and inhibits the activation of TLR4/MyD88/NF-κB signaling pathway, which might be regulated by excessive desulfobacterota in diabetic mice. We also found that LDR could ameliorates metabolic abnormalities in T2DM. Importantly, our studies have uncovered a novel mechanism of protective roles by LDR in T2DM.

2.1 T2DM model establishment and LDR exposure

Male wild-type C57BL/6 mice (SPF, 16 ~ 22 g, 6 ~ 8 weeks) were supplied by Beijing HFK Bioscience CO. LTD. (Beijing, China). The mice bred at the laboratory Animal Center of Public Health School of Jilin University (license No. SYXK (Ji) 2021–0003). The study protocol was approved by the Medical Ethics Committee of Jilin University and was conducted in accordance with the Institutional Guide for the Ethical Use of Animals (2021-08-01). The experiment was started after acclimation to laboratory conditions for 1 week. Eighty mice were randomized into vehicle and T2DM groups, 20 mice in vehicle group were fed by a low-fat diet (LFD, 10% fat), and 60 mice in T2DM group were fed by high-fat diet (HFD, 60% fat) for 16 weeks, respectively. HFD-fed mice were intraperitoneally injected with Streptozotocin (STZ) to (for 5 consecutive days, 30 mg/kg body weight; dissolved in 0.1 M citrate buffer, pH4.5) to induce T2MD [32]. Mouse with random blood glucose levels over 11.1 M measured 4 days after STZ injection were considered diabetic. Subsequently, the LFD-fed mice and diabetic mice were randomly divided into 4 subgroups, including Control (LFD), LDR (LFD + 75 mGy), T2MD (HFD) and T2MD + LDR (HFD + 75 mGy). For LDR subgroup, mice were exposed to75 mGy each time (dose rate: 13.4 mGy/min),, exposure were performed every other day for 1 month, total dose was 1.05 Gy, the doses and rates were selected based on a report by the United Nations Scientific Committee on Atomic Radiation (UNSCAR, 1986) and on previous studies ^[33]. All mice were kept in a specific pathogen-free environment at room temperature ( 24 ± 1℃ ), relative humidity (25 ~ 40%), and a 12 h light/dark cycle. All mice were provided with a corresponding diet and body weight was detected weekly at the end of the experiments.

2.2 Reagents and Antibodies

STZ were purchased from Sigma-Aldrich (St Louis, MO USA). IL-4 (APC, 17-7041-82) and IL-17A (APC, 17-7177-81) was obtained from eBioscience (San Diego, CA, USA). MyD88 (BS3521), TLR4 (BS91353) and β-Actin (BS6007M) was obtained from Bioworld (Bloomington, MN, USA). p-NF-κB-p65 (YT0192), p-p38 (YP0338), HRP* goat anti-rabbit IgG (H + L) (RS0002) and HRP* goat anti-mouse IgG (H + L) (RS0001) was obtained from Immunoway (Plano, TX, USA). TRIzol RNA isolation reagent was purchased from Invitrogen (Carlsbad, CA, USA). Other chemicals and reagents were analytic grade.

2.3 Glucose Tolerance Test

Food was withheld 16 h before testing, mice (HFD) were administrated with glucose (1.5 mg/g) by intraperitoneal injection with 5% glucose. Blood samples were taken by tail pick at 0, 15, 30, 60, 90 and 120 min after glucose injection, glucose levels were measuered using glucometer (Sinocare, Hunan, China).

2.4 Histological and immunohistochemical analysis

Pancreatic and intestinal tissues were fixed with 4% paraformaldehyde (PFA) for more than 48 h at room temperature and processed for paraffin embedding. Subsequently, four µm sections were prepared for hematoxylin and eosin (H&E) and immunohistochemical analysis. Pancreatic sections were stained with H&E to routine histopathological examination as well as morphometric analysis at 200 magnification. Immunohistochemistry examination was used to detect TLR4, MyD88, p-NF-κB and p-p38 in intestinal tissues, HRP-conjugated secondary antibody was added after primary antibodies incubatiob, and samples were incubated for 10 min at room temperature. Then 3, 3′-diamino-benzidine (DAB) color solution was added, and nucleus was stained with hematoxylin for 3 min before dehydration. Then, the tissue sections were sealed using neutral gum. Under the light microscopy, DAB positive expression was brownish-yellow and the nucleus was blue. Photographs were taken under a microscopy.

2.5 16S rDNA sequencing

Mice fecal samples were collected from the Control, LDR, T2MD and T2MD + LDR groups, respectively. The total fecal microbiota DNA was extracted from fecal samples using a QIAamp Fast DNA stool Mini Kit (Qiagen, Cat# 51604) and full length sequence of 16S rDNA was amplified by polymerase chain reaction (PCR). The denoised sequences were binned into operational taxonomic units (OTUs) with 97% similarity using USEARCH (version 10.0). Taxonomy was assigned to all OTUs by searching against the Silva databases (Release128) using QIIME software. The difference of gut microbiota composition were further analyzed, and the relative abundance curve was used to reflect species diversity and abundance of samples. Alpha-diversity indexes, including ACE and Chaos and Simpson, were performed to comparably analyze the species richness and uniformity. Beta-diversity was used to evaluate the species complexity among different groups. The distribution of gut microbiota at the class level in different groups were compared, and the characteristic strains were researched by Linear discriminant analysis Effect Size (LEfSe), OTUs with relative abundance greater than 0.5% were screened for analysis based on Linear Discriminant Analysis (LDA) score. Correlation analysis were performed using “Spearman” (|r| ≥ 0.1, P < 0.05), and functional prediction were performed using “PICRUSt2” R package to analyze the differential component in KEGG pathways in different samples or groups, the difference in functional genes and their effects on metabolic pathways can be assessed.

2.6 Untargeted metabomics profiling

An equal volume of serum from mice in each group were used to metabolite extraction. Untargeted metabolomics detection was performed using a Waters ACQUITY UPLC I-Class and Xevo G2-XS QTOF system. Peak extraction and alignment were performed using the Progenesis QI software. Untargeted metabolomics analysis was performed by Principal Component Analysis (PCA), Difference multiple analysis, metabolite annotation and receiver operating characteristic curve (ROC), etc.

2.7 Flow Cytometry

Fresh spleen tissues were collected from each group and washed by PBS, the the single cell suspension was prepared. The cell concentration was adjusted to 1×10⁷ cells/mL, and the cells were washed using 1mL PBS. After cells were fixed and membranes were broken, 5µL IL-4 (APC) and IL-17A (APC) antibodies were added and mixed according to the manufacturer's protocols. The cells were stained in the dark for 30 min and were washedusing PBS. Furthermore, 10000 cells were collected by BD Flow Cytometry (Franklin Lakes, NJ, USA) and analyzed by FlowJo software.

2.8 Quantitative real-time PCR (qRT-PCR)

Total RNA of mice intestine was extracted by TRIzol reagent according the manufacturer's protocols. The concentration of RNA was determined and cDNA was synthesized. The mRNA expression levels were detected by qRT-PCR using SYBR Green master mix obtained from TransGen Biotech Co. (Beijing, China).

The following primers were synthesized by Kumei Inc. (Jilin, China), GAPDH: 5′-AAATGGTGAAGGTCGGTGTG-3′ (F), 5′-TGAAGGGGTCGTTGATGG-3′ (R); TLR4: 5′-ATGGCATGGCTTACACCACC-3′ (F), 5′-GAGGCCAATTTTGTCTCCACA-3′(R); MyD88: 5′-ATCGCTGTTCTTGAACCCTCG-3′ (F), 5′-CTCACGGTCTAACAAGGCCAG-3′ (R); NF-κB: 5′-ATGGCAGACGATGATCCCTAC-3′ (F), 5′-CGGAATCGAAATCCCCTCTGTT-3′ (R); TNF-α: 5′-CTACCTTGTTGCCTCCTCTTT-3′ (F), 5′-CGATCACCCCGAAGTTCAGTAG-3′ (R); IL-1: 5′-AGTATCAGCAACGTCAAGCAA-3′ (F), 5′-TCCAGATCATGGGTTATGGACTG-3′ (R). The relative expressions of mRNAs were calculated and quantified using the 2-△△CT method.

2.9 Statistical analysis

All statistical analysis were performed using SPSS software (version 24.0, SPSS Inc., Chicago, IL, USA). The results were presented as mean ± SEM and subjected to Two-way ANOVA followed by Student’s t-tests, P < 0.05 indicates significant difference.

3.1 Induction of T2DM and ameliorated effects of LDR

After 12-weeks feeding with HFD, the weight of mice increased obviously compared with control group, until 16 weeks, weight increased slowly (Fig. 1a). The glucose tolerance test showed that the blood glucose of the HFD-fed mice reached the maximum level at 30 min after intraperitoneal injection, and the glucose level of HFD group were significantly higher than control group (Fig. 1b). In order to establish a typical T2DM mice model, STZ was intraperitoneally injected to the HFD-fed mice. The results in Fig. 1c shown that the blood sugar value reached for 11.66 ± 0.86 mmol/L in T2DM group, indicating the successful establishment of T2DM mice model. Subsequently, the mice exposure to the LDR according to the workflow shown in Fig. 1d. LDR intervention significantly reduced blood glucose level as compared with T2DM group (Fig. 1e). HE staining showed that islet architecture was complete in control group, with clear boundary, and the β cell nucleus was round, whereas the islet structure was damage seriously in T2DM mice, islets of T2DM mice were accompanied with scattered inflammatory cell aggregated. LDR intervention displayed a markedly protective effect on the size and structure of the islet (Fig. 1F). Meanwhile, the body weight decreased in T2DM mice after LDR exposure (Fig. 1g).

These results suggest that LDR intervention can alleviate the severity of T2DM.

3.2 LDR ameliorates gut microbiota dysbiosis induced by T2DM

To study the effects of LDR on gut microbiota composition in T2DM mice, 16s rDNA sequencing was performed to evaluate and analyze relative changes. Venn diagram showed the core microbiome in the different groups comparing the unique/shared OTUs in the gut microbiota of the mice, the number of species was 287, 208, 308 and 215 in Con, LDR, T2DM and LDR + T2DM groups, respectively (Fig. 2A). The overlapping OTUs between T2DM and LDR + T2DM groups was 174, indicating LDR markedly changed the numbers of OTUs in the gut microbiota (Fig. 2a and Fig. S1a). The outcome of principal coordinate analysis (PCoA) showed significant differences among these four groups (Fig. 2b). Furthermore, we measured the community richness, as shown in Fig. 2c and Fig. S1b, the top 10 differential microbioats in class level and species level had significant shifts in proportional between T2DM and LDR + T2DM groups. In particular, the abundance of Desulfovibrionia and Desulfovibrio was dramatically higher in which group than that in T2DM groups. To further determine the difference between the T2DM and LDR + T2DM, differential analysis between groups was performed at class level, the Desulfovibrionia was significantly decreased in LDR + T2DM groups compared with T2DM groups (Fig. 2d). Alpha diversity was used to measure the richness of species in a community, the ACE and Chaos index was profoundly lower in T2DM group than other groups, whereas Shannon index showed no difference (Fig. 2e). Additionally, we analyzed the relative abundance of Bacilli and Desulfovibrionaceae, the results showed that Bacilli decreased and Desulfovibrionaceae increased in T2DM groups, LDR exposure could ameliorate these changes (Fig. 2f). The network diagram is a typical way to present correlation analysis, the Prevotellaceae_UCG_001 was negatively correlated with Desulfovibrio at phylum level (P = 1.474e-06, R² = 0.905, Fig. S1c). KEGG pathway enrichment analysis showed that Desulfovibrionia related to 43 metabolic pathways at Class level (Fig. S1d). These results

suggests that LDR can ameliorate the increase of Desulfovibrionia in T2DM.

3.3 LDR altered metabolites in T2DM identified by untargeted metabolomics profiling under positive ion mode

In this study, the metabolites were measured under positive and negative ion mode, and further analyzed the correlation between metabolites and gut microbiota. As venn graph shown in Fig. 3a, 3 metabolites commonly existed in 4 groups under positive ion mode. In addition, PCA analysis of metabolites found that the metabolites were similar in Con and LDR groups, while the difference is relatively low, however, there was a significantly difference in metabolic products between T2DM and Con group (Fig. 3b). As compared with Con group, 31 up-regulated and 45 down-regulated metabolites were screened in LDR group, while 344 up-regulated and 390 down-regulated metabolites were screened in T2DM group. Additionally, compared with T2DM group, 221 up-regulated and 176 down-regulated metabolites were screened in LDR + T2DM group (Fig. 3c and Fig. S2a). As shown in Fig. 3D and Figure S2B, KEGG enrichment analysis found that the metabolomics mainly accumulated in mineral absorption, choline metabolism in cancer, glycerophospholipid metabolism and ABC transporters, etc. In consist with it, the network diagram also showed similar enrichment regularity (Fig. 3e and Fig. S2c). Compared with T2DM group, Xanthine, Kaempferol3-O-ferulogy-caffeoyl-sophoroside 7-O-glucoside, 3-Phenyl-1H-pyrazole-4-carbaldehyde and PE (22:1(13Z)/PGE1) significantly increased (Fig. 3f).

3.4 LDR altered metabolites in T2DM identified by untargeted metabolomics profiling under negative ion mode

Additionally, under the negative ion mode, there were 13 metabolites commonly found among Con, T2DM and LDR + T2DM groups (Fig. 4a). As shown in Fig. 4b, in T2DM and LDR + T2DM groups, KEGG enrichment analysis found that the metabolomics mainly accumulated in flavonoid biosynthesis, biosynthesis of amino acids, flavone and flavonol biosynthesis, cysteine and methionine metabolism, etc. Compared with Con group, 18 up-regulated and 18 down-regulated metabolites were screened in T2DM. Compared with T2DM group, 64 up-regulated and 20 down-regulated metabolites were screened in LDR + T2DM group (Fig. 4c). As shown in Fig. 4d, the network diagram showed similar enrichment regularity. As shown in Fig. 4e, combined analysis of microbiota and metabolomics found that OTUs in the gut microbiota were associated with metabolites.

3.5 The inflammation was alleviated by LDR through inhibiting TLR4/MyD88/NF-κB pathway in T2DM

IL-4 and IL-17A expression in spleen cells were detected by Flow Cytometry, as shown in Fig. 5A and b, IL-4 positive cells were significantly decreased, while IL-17A positive cells were significantly increased in T2DM groups (P < 0.05). However, these changes were alleviated in LDR + T2DM groups, suggesting that LDR could activate the immune response. Furthermore, we measured the expressions of TLR4, MyD88, p-NF-κB and p-p38 in intestine tissue by IHC. Compared with Con, these protein expressions were increased in T2DM group, while they were decreased in LDR + T2DM group, suggesting the activation of TLR4/MyD88/NF-κB pathway in T2DM, and LDR inhibited thispathway (Fig. 5c). In line with it, the qRT-PCR results showed the mRNA expressions of TNF-α and IL-1 also were increased in T2DM group but decreased in LDR + T2DM group (Fig. 5d).

Recent studies have highlighted the pivotal role of the low dose radiation (LDR) in diabetes mellitus ^{[34, 35]}. Clinical cases showed that the patient’s condition improved, and high glucose levels reduced to normal level following radon therapy, the case indicated the potential improvement of type 1 diabetes treated by LDR ^[36]. Additionally, several reports described the mitigation of diabetes following LDR exposure, for instance, Tsuruga et al. ^[37] showed that LDR (γ-rays) exposure to diabetic mice resulted in the improved glucose clearance and attenuation in pancreatic islet degeneration. Guo et al. ^[38] found that repeated LDR (75 mGy X-ray) treated diabetic animals showed quicker skin wound healing. Animal models and clinical observations suggest that LDR can alleviate diabetes, however, the mechanism is the subject of further investigation. In this study, we found that repeated LDR (75 mGy X-ray, dose rate = 0.0134 Gy/min) exposure to the type 2 diabetes mellitus (T2DM) induced by high-fat diet and STZ can decrease the glucose level, which is similar to previous reports.

The gut microbiota is a complex ecosystem made up of a community of microorganisms, the primary characteristics of the gut microbiota dysbiosis is the decreased diversity and abundance. This can lead to the emergence of obesity, metabolic disorders and T2DM ^{[39, 40]}. The patients with T2DM would also have some alterations of the gut microbiota, including the decreased Lactobacillus, Clostridium, and Bifidobacterium genera, while the increased Escherichia coli, Enterococcus, Bacteroidetes, and Desulfovibrio ^{[41, 42]}. In this study, the bacterial diversity of the gut microbiota in T2DM group was changed, especially, in Bacilli, Desulfobacterota, Verrucomicrobiota and Proteobacteria. Howerver, for T2DM mice exposed to LDR, the diversity changes of Bacilli and Desulfobacterota were ameliorated, which indicates that LDR could increase the beneficial bacteria and inhibit the harmful bacteria. Some studies consistently showed that the Proteobacteria, Verrucomicrobia, Alistipes, and Akkermancia were relatively more abundant after ionizing radiation exposure, whereas Bacteroidetes, Firmicutes, and Lactobacillus were relatively reduced ^[43]. Thus, it would be useful to develop a strategy for modifying gut microbiota to prevent the T2DM.

Additionally, gut microbiota and its metabolites might involve in the process of T2DM. Studies have indicated that patients with T2DM are often associated with intestinal flora disorders and dysfunction, and the metabolites of gut microbiota, such as bile acids (BAs), short-chain fatty acids (SCFAs) and amino acids, may result in insulin sensitivity and regulate the immune homeostasis ^[44]. In this study, we found that metabolites detected in the blood of T2DM mice induced by high-fat diet were obviously different, which manifest as the mineral absorption, choline metabolism in cancer, glycerophospholipid metabolism and ABC transporters, flavonoid biosynthesis, biosynthesis of amino acids, flavone and flavonol biosynthesis, cysteine and methionine metabolism, etc. T2DM is one of the most common metabolic disorders. Gut microbiota can modulate the gut barrier integrity and human metabolism to take part in the synthesis of metabolites. Nevertheless, LDR may play roles in improving the metabolic patterns of T2DM, thereby easing the damage of diabetes. To investigate how LDR ameliorates the impairment of T2DM, we analyzed the relationships between gut microbiota and metabolites, and the results showed that LDR could increase the abundance of Bacilli, and decrease the abundance of Desulfovibrionaceae to produce crucial metabolites, such as SCFAs, LPS and amino acids. LPS is the main product of Desulfovibrio, and it can induce inflammation and insulin resistance through TLRs signaling pathway ^[21]. In this study, we found that the TLR4/MyD88/NF-κB pathway was activated in intestine tissue of HFD induced T2DM mice model, furthermore, the TNF-α and IL-1 increased in intestine. In addition, in spleen, the percentage of IL-4⁺ cells decreased, while the percentage of IL-17A⁺ cells increased, which were ameliorated by LDR. These data indicate that the impact of LDR on gut microbiota, metabolism and inflammation reaction may be the mechanisms of LDR ameliorating T2DM impairment.

However, several topics still need to explore. First, Linear no-threshold (LNT) model assumes that even very low doses radiation has adverse effects on human health, but another voice demonstrates that the unique biological effect caused by LDR is beneficial ^[45,46]. Previous studies have showed that LDR played the protective roles in Diabetic impairments ^[24–26], however, there were different voice about the protection ^[47]. Actually, we used lower exposure doses and dose rates (0.0134 Gy/min), and there were 48 h for damage repair, it may be a possible reason for difference among experiments. Overall, we have demonstrated a critical role of LDR in protecting HFD-induced T2DM from processing through gut microbiota and metabolism. Especially, Desulfovibrio produce LPS to activate the TLR4/MyD88/NF-κB pathway to mediate the inflammation, which might be impaired by the LDR (Fig. 6).

Author contributions

Conception, design, writing review and editing: Zhicheng Wang, Huan He. Design, investigation, validation, writing original draft: Lijing Qin, Rongrong Liu. Data curation, supervision, investigation: Zhen Jia, Weiqiang Xu. Resources, data curation: Li Wang, Hongyuan Tian. Software: Xinru Lian Wen Li, Yali Qi. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

This work was supported by the Science and Technology Development Plan of Jilin (20240101275JC, YDZJ202201ZYTS153 and YDZJ202401200ZYTS).

Ethics declarations

No.

Data availability

All data generated or analyzed during this study are included in this published article.

Disclosure

The authors report no conflicts of interest in this work.

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No competing interests reported.

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Multiple Low-Dose Radiation ameliorates type-2 diabetes mellitus via gut microbiota modulation to activate TLR4/MyD88/NF-κB pathway

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Abstract

Background

Method

Results

Figures

1. INTRODUCTION

2. METERIALS AND METHODS

2.1 T2DM model establishment and LDR exposure

2.2 Reagents and Antibodies

2.3 Glucose Tolerance Test

2.4 Histological and immunohistochemical analysis

2.5 16S rDNA sequencing

2.6 Untargeted metabomics profiling

2.7 Flow Cytometry

2.8 Quantitative real-time PCR (qRT-PCR)

2.9 Statistical analysis

3. RESULTS

3.1 Induction of T2DM and ameliorated effects of LDR

3.2 LDR ameliorates gut microbiota dysbiosis induced by T2DM

3.3 LDR altered metabolites in T2DM identified by untargeted metabolomics profiling under positive ion mode

3.4 LDR altered metabolites in T2DM identified by untargeted metabolomics profiling under negative ion mode

3.5 The inflammation was alleviated by LDR through inhibiting TLR4/MyD88/NF-κB pathway in T2DM

4. DISCUSSION

Declarations

References

Additional Declarations

Supplementary Files

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