Open Access
Immunology and Microbiology  |   August 2021
Dysbiosis in the Gut Microbiome in Streptozotocin-Induced Diabetes Rats and Follow-Up During Retinal Changes
Author Affiliations & Notes
  • Shalem Raj Padakandla
    Prof Brien Holden Eye Research Centre, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
  • Taraprasad Das
    Smt. Kanuri Santhamma Centre for Vitreo Retinal Diseases, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
  • Gumpili Sai Prashanthi
    Prof Brien Holden Eye Research Centre, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
  • Kiran Kumar Angadi
    Biochemistry Division, ICMR-National Institute of Nutrition, Hyderabad, Telangana, India
  • S. Sreenivasa Reddy
    Biochemistry Division, ICMR-National Institute of Nutrition, Hyderabad, Telangana, India
  • G. Bhanuprakash Reddy
    Biochemistry Division, ICMR-National Institute of Nutrition, Hyderabad, Telangana, India
  • Sisinthy Shivaji
    Prof Brien Holden Eye Research Centre, L. V. Prasad Eye Institute, Hyderabad, Telangana, India
  • Correspondence: Sisinthy Shivaji, Prof Brien Holden Eye Research Centre, L. V. Prasad Eye Institute, Kallam Anji Reddy campus, Hyderabad, Telangana 500034, India; shivas@lvpei.org
Investigative Ophthalmology & Visual Science August 2021, Vol.62, 31. doi:https://doi.org/10.1167/iovs.62.10.31
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      Shalem Raj Padakandla, Taraprasad Das, Gumpili Sai Prashanthi, Kiran Kumar Angadi, S. Sreenivasa Reddy, G. Bhanuprakash Reddy, Sisinthy Shivaji; Dysbiosis in the Gut Microbiome in Streptozotocin-Induced Diabetes Rats and Follow-Up During Retinal Changes. Invest. Ophthalmol. Vis. Sci. 2021;62(10):31. doi: https://doi.org/10.1167/iovs.62.10.31.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: To analyze the gut bacterial microbiome of streptozotocin-induced diabetic rats and rats with retinal changes.

Methods: Induction of diabetes was confirmed by an increase in blood sugar (>150 mg/dL), and the progression of diabetes with retinal changes was assessed by histology and immunohistochemistry of retinal sections. Microbiomes were generated using fecal DNA, and the V3–V4 amplicons were sequenced and analyzed by QIIME and R.

Results: Dysbiosis in the gut microbiome of diabetic rats and diabetic rats with retinal changes was observed at the phylum and genus levels compared with the control rats. Heat-map analysis based on the differentially abundant genera indicated that the microbiomes of controls and diabetic rats separated into two distinct clusters. The majority of the microbiomes in diabetic rats with retinal changes also formed a distinct cluster from the control rats. β-diversity analysis separated the microbiome of control rats from the microbiome of diabetic rats and diabetic rats with retinal changes, but the microbiomes of diabetic rats and diabetic rats with retinal changes showed an overlap. Functional analysis indicated that the enhanced inflammation in diabetic rats showing retinal changes could be ascribed to a decrease in anti-inflammatory bacteria and an increase in pathogenic and proinflammatory bacteria.

Conclusions: This study showed that the gut bacterial microbiome in diabetic rats with retinal changes was different compared with control rats. The results could help develop novel therapeutics for diabetics and diabetic individuals with retinal changes.

Diabetes mellitus (DM) is a chronic metabolic disorder of major public health importance. Currently, 463 million adults are estimated to have DM, and it is expected that the number will increase to 700 million by 2045.1,2 In India, the prevalence of diabetes in adults is 8.9%, and an estimated 77 million people in India have diabetes.3 Diabetic retinopathy (DR) is the most common ophthalmic manifestation of people with DM. DR is similar in patients with type 1 DM (T1DM) and those with type 2 DM (T2DM).4,5 About 30% of people with DM have DR, which is the leading cause of visual impairment in working-age people worldwide.6,7 The prevalence of DR among diabetics in India varied from 9.6 to 21.7% over the last decade.8 
The gut bacterial microbiome is a dynamic community of bacteria, and dysbiosis (changes in diversity and function) in the gut microbiome has been associated with several autoimmune and inflammatory diseases.912 The association of gut microbiome dysbiosis with ocular diseases, such as Sjögren syndrome, uveitis, age-related macular degeneration, and fungal and bacterial keratitis,1315 has been described. However, very few studies have addressed changes in the gut microbiome during the progression of DM to DR. Recently, our group demonstrated dysbiosis in the gut microbiomes and mycobiomes (at both the phylum and genus level) in people with T2DM and DR compared with healthy controls.16,17 We identified specific bacteria and fungi associated with T2DM and DR. Such changes in the gut microbiome could also be studied in rodent model systems.18,19 
Streptozotocin (STZ)-induced diabetic mice and rats have been used as models to study the underlying mechanisms of DR.18,9 Rodents mimic the development of DR and exhibit early cellular and vascular abnormalities, but subsequent retinal changes as a consequence of prolonged hyperglycemia do not manifest in rodents or other model animals (e.g., cats, dogs, zebrafish).18,19 A recent study confirmed dysbiosis at the genus level in the microbiome of diabetic mice (db/db mice).20 Simultaneously, these mice presented certain features of DR, such as acellular capillaries, activation of retinal microglia, and infiltration of peripheral immune cells into the retina.20 In the present study, we compared the gut bacterial microbiome (henceforth microbiome) of healthy rats with STZ-induced DM and diabetic rats with retinal changes (DRC). The study had two objectives: (1) to identify the microbiome associated with DM rats, and (2) to ascertain whether microbiome dynamics are responsible for the progression of DM to DRC. We also monitored longitudinal changes in the gut microbiome in DM rats to understand the temporal development of DM and DRC. The similarities and differences that highlight the novelty of the current study are highlighted in the discussion section. 
Materials and Methods
Animals
Sprague Dawley rats were obtained from National Institute of Nutrition (Hyderabad, India) following approval by the Institutional Animal Ethics Committee (P9F/IAEC/NIN/5/2018/GBP/SD-98M). Individual animals were housed in polypropylene cages at 22°C ± 2°C at 50% humidity and under a 12-hour light/dark cycle. Food and water were provided ad libitum. 
Induction of Diabetes and Retinal Changes
Forty-eight animals, 3 months old and with an average body weight of 230 ± 14 g, were recruited together. The control groups (CT1–CT4) were injected with 0.1-M sodium citrate buffer, pH 4.5, as a vehicle21,22 and were sacrificed after 1, 2, 3, and 4 months, respectively. DM was induced in 24 rats by injecting STZ (35 mg/kg) intraperitoneally.21,22 DM1 and DM2 rats were sacrificed 1 and 2 months, respectively, after STZ injection, and CT1 and CT2 served as the controls. DRC 1 and DRC2 rats were sacrificed 3 and 4 months, respectively, after STZ injection, and CT3 and CT4 served as the controls. Glucose levels were quantitated using a drop of blood from the tail vein using an ACCU-CHEK Active Blood Glucose Meter Kit (Roche Diagnostics, Basel, Switzerland). 
Preparation of Retinal Sections
Animals in the eight groups (CT1–CT4, DM1 and DM2, and DRC1 and DRC2) were allowed to fast overnight and were sacrificed by CO2 asphyxiation. Eyeballs were fixed in 10% buffered formalin, embedded in paraffin blocks, and sectioned and mounted on glass slides as described earlier.21,22 
Hematoxylin and Eosin Staining
Retinal sections were deparaffinized and stained with hematoxylin for 10 minutes following our earlier protocol.21,22 
Immunohistochemistry
Immunohistochemical detection of VEGF and rhodopsin in retinal sections using VEGF- and rhodopsin-specific antibodies (ab32152 and ab232934, respectively; Abcam, Cambridge, UK) was done according to our earlier procedure.21,22 
Imaging
All sections were observed at 40× magnification using an Aperio AT2 slide scanner (Leica Biosystems, Wetzlar, Germany). 
Immunoblotting of Hypoxia-Inducible Factor-1 Alpha
Retinal cellular protein was extracted according to Reddy et al.23 and quantified by Lowry's method,24 and equal protein concentrations were resolved by electrophoresis (12% sodium dodecyl sulfate–polyacrylamide gel). Proteins were then transferred onto a nitrocellulose membrane, and immunoblotting of hypoxia-inducible factor 1-alpha (HIF-1α) protein was performed with HIF-1α primary antibody as described earlier.22 Tubulin served as a loading control. 
Fecal Sample Collection and DNA Extraction
Fecal pellets (five or six) were collected from each animal in a cryotube and frozen at –80°C for future use. Genomic DNA was extracted from ∼150 mg of homogenized fecal pellets, and the quality of DNA was checked as described earlier.1317 
Illumina Library Preparation and Sequencing of the Bacterial Microbiome
Amplification of the V3–V4 region of bacterial 16S rRNA genes was performed as described earlier.13 Bacterial microbiomes were generated following the standard Illumina (San Diego, CA, USA) protocol13,25 and sequenced using 2 × 250-bp chemistry on an Illumina MiSeq system with a paired-end protocol at Xcelris Genomics Pvt. Ltd. (Ahmedabad, India). 
Taxonomic Classification of Sequenced Reads
Paired-end reads were demultiplexed and merged using FLASH (https://ccb.jhu.edu/software/FLASH/). Reads with a mean Phred score < 25 and chimeric sequences were removed using Prinseq-lite (http://prinseq.sourceforge.net/) and Usearch61 (http://www.drive5.com/usearch/download.html), respectively. Only high-quality (HQ) reads were used to select operational taxonomic units (OTUs) using QIIME (http://qiime.org/). SILVA 138 SSU (https://www.arb-silva.de/documentation/release-138/) was used as the reference OTU database to identify bacteria at 97% identity, and the assignment of taxonomy was made as described in our earlier studies.17,25 Sparse OTUs (<0.001% of the total HQ reads) were not considered. 
Diversity Analyses of Bacterial Microbiome Samples and Statistical Analysis
Rarefaction curves and alpha diversity indices (Shannon diversity index, Simpson index, observed number of OTUs, and Chao1) for the microbiomes were generated using the vegan 2.4-2 package (http://vegan.r-forge.r-project.org/) in R (R Foundation for Statistical Computing, Vienna, Austria). The microbiome data were analyzed using R 3.3.2. 
Identification of Differentially Abundant Taxonomic Groups
Kruskal–Wallis and Wilcoxon signed-rank tests (with P < 0.05 being significant) were implemented to identify significantly different genera in the CT, DM, and DRC microbiomes. A two-dimensional heat map, with rank-normalized abundances of the differentially abundant bacterial genera, was plotted in R. Differences in the gut bacterial microbiomes were also visualized using a non-metric multidimensional scaling (NMDS) plot based on Bray–Curtis distance dissimilarity. 
Interaction Networks Among Bacterial Genera in the Gut Microbiomes
Interaction networks were generated separately for the CT, DM, and DRC microbiomes using CoNet (https://raeslab.org/software/conet.html) in Cytoscape (https://cytoscape.org/). 
Results
Induction of Diabetes and Fasting Blood Sugar
Diabetes was induced in Sprague Dawley rats through intraperitoneal injection of STZ. The average fasting blood glucose in the DM and DRC rats was >150 mg/dL after 72 hours and persisted at >150 mg/dL after 1, 2, 3, and 4 months of STZ injection (Fig. 1). CT animals did not exhibit any significant changes in the blood glucose levels. 
Figure 1.
 
Average blood sugar levels of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2).
Figure 1.
 
Average blood sugar levels of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2).
Histology and Immunohistochemistry
In this study, the third- and fourth-month DRC1 and DRC2 rats had increased levels of blood glucose (>400 mg/dL) (Fig. 1) and also showed a decrease in the number of cells in the inner nuclear layer and the outer nuclear layer compared with the CT3 and CT4 rats, respectively (Fig. 2). Further, HIF-1α increased significantly in the retina of DRC1 rats compared with CT3 rats, whereas VEGF increased in DM2, DRC1, and DRC2 rats compared with CT2 to CT4 animals. Intense staining of VEGF was also visible in the inner plexiform layer, outer plexiform layer and the layer between the photoreceptor layer and outer nuclear layer (Figs. 3A, 3B). A decrease in rhodopsin in the photoreceptor layer was observed in the DM and DRC groups compared with the CT rats (Fig. 3C). 
Figure 2.
 
Hematoxylin and eosin–stained retinal sections of control (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). Scale bar: 20 µm. GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; PRL, photoreceptor layer.
Figure 2.
 
Hematoxylin and eosin–stained retinal sections of control (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). Scale bar: 20 µm. GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; PRL, photoreceptor layer.
Figure 3.
 
(AC) Quantitative expression of HIF-1α by immunoblotting (A) and VEGF (B) and rhodopsin (C) by immunohistochemistry using specific antibodies in the retina of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). In A, levels of HIF-1α were calculated using tubulin as a control, and the data (mean ± SEM of three independent experiments) are represented as percentage of expression compared with control rats. In B and C, VEGF- and rhodopsin-specific antibodies were used for immunohistochemical staining. Arrows indicate either the increased production of VEGF or decreased production of rhodopsin. Scale bar: 20 µm. P < 0.05 was considered significant.
Figure 3.
 
(AC) Quantitative expression of HIF-1α by immunoblotting (A) and VEGF (B) and rhodopsin (C) by immunohistochemistry using specific antibodies in the retina of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). In A, levels of HIF-1α were calculated using tubulin as a control, and the data (mean ± SEM of three independent experiments) are represented as percentage of expression compared with control rats. In B and C, VEGF- and rhodopsin-specific antibodies were used for immunohistochemical staining. Arrows indicate either the increased production of VEGF or decreased production of rhodopsin. Scale bar: 20 µm. P < 0.05 was considered significant.
Figure 4.
 
Rarefaction curves and (B) alpha diversity indices (Shannon diversity index, Simpson index, number of observed OTUs, and Chao1 index) of the gut bacterial microbiomes of control rats (CT, n = 24), diabetic rats (DM, n = 12), and diabetic rats showing retinal changes (DRC, n = 11). Asterisks indicate significant differences between CT and DM rats (*) and between CT and DRC rats (**), as determined by Student's t-test (P < 0.05).
Figure 4.
 
Rarefaction curves and (B) alpha diversity indices (Shannon diversity index, Simpson index, number of observed OTUs, and Chao1 index) of the gut bacterial microbiomes of control rats (CT, n = 24), diabetic rats (DM, n = 12), and diabetic rats showing retinal changes (DRC, n = 11). Asterisks indicate significant differences between CT and DM rats (*) and between CT and DRC rats (**), as determined by Student's t-test (P < 0.05).
Analysis of OTUs of the Gut Bacterial Microbiomes of Control, Diabetic, and Diabetic Rats Showing Retinal Changes
Average HQ reads per microbiome from the CT, DM, and DRC rats were 393,670, 464,123, and 441,727, respectively. From these microbiomes, 3036 OTUs were identified (1307 reference and 1729 de novo OTUs) (Supplementary Table S1). The saturation of rarefaction curves indicated that the sequencing depth and coverage were adequate to capture the total diversity in the 47 microbiomes (Fig. 4A). Alpha diversity analysis indicated that the Shannon diversity index and Simpson index differed significantly (P < 0.05) between the DM and DRC cohorts compared with the control but the number of observed OTUs and the Chao1 index were not significantly different (Fig. 4B). 
Bacterial Community Composition
In the gut bacterial microbiomes of rats, 16 different phyla, excluding unclassified phyla, were detected (see Tables 2 and 3). The abundance of Bacteroidetes, Firmicutes, Euryarchaeota, Spirochaetes, and Proteobacteria was >0.50% in the eight cohorts, and Bacteroidetes and Firmicutes were the most abundant (Fig. 5; see also Tables 2 and 3). We observed longitudinal changes in abundance at the phylum level for CT1 to CT4, DM1 and DM2, and DRC1 and DRC2 (Figs. 5A–5C). DM1 differed from CT1 in the median abundance of Actinobacteria, Planctomycetes, Proteobacteria, Spirochaetes, and WPS2 (Table 1). However, after 2 months, DM2 differed from CT2 only in the median abundance of Lentisphaerae (Table 1). Further, in DRC1, four phyla (namely, Cyanobacteria, Euryarchaeota, Firmicutes, and Spirochaetes) showed a significant difference, whereas in DRC2 Euryarchaeota and Spirochaetes continued to show a significant difference in abundance, along with Actinobacteria and Proteobacteria, when compared with CT3 and CT4, respectively (Table 2). 
Figure 5.
 
(A) Abundance (%) of phyla in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). (B) Median abundance of bacterial phyla from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). CT1, CT2, CT3, and CT4 refer to control rats after 1, 2, 3, and 4 months, respectively; DM1 and DM2 refer to rats after 1 and 2 months, respectively, of STZ treatment; and DRC1 and DRC2 refer to rats after 1 and 2 months, respectively, of detection of retinal changes.
Figure 5.
 
(A) Abundance (%) of phyla in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). (B) Median abundance of bacterial phyla from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). CT1, CT2, CT3, and CT4 refer to control rats after 1, 2, 3, and 4 months, respectively; DM1 and DM2 refer to rats after 1 and 2 months, respectively, of STZ treatment; and DRC1 and DRC2 refer to rats after 1 and 2 months, respectively, of detection of retinal changes.
Table 1.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 1 Month (CT1, n = 6) and 2 Months (CT2, n = 6) and Diabetic Rats After 1 Month (DM1, n = 6) and 2 Months (DM2, n = 6)
Table 1.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 1 Month (CT1, n = 6) and 2 Months (CT2, n = 6) and Diabetic Rats After 1 Month (DM1, n = 6) and 2 Months (DM2, n = 6)
Table 2.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 3 Months (CT3, n = 6) and 4 Months (CT4, n = 6) and Diabetic Rats Showing Retinal Changes After 1 Month (DRC1, n = 6) and 2 Months (DRC2, n = 6)
Table 2.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 3 Months (CT3, n = 6) and 4 Months (CT4, n = 6) and Diabetic Rats Showing Retinal Changes After 1 Month (DRC1, n = 6) and 2 Months (DRC2, n = 6)
At the genus level, 136, 137, and 136 genera were identified in CT1 to CT4, DM1 and DM2, and DRC1 and DRC2 rats (Supplementary Table S1). The most abundant genera, with average abundance of >1% in any one of the three groups, are listed in Supplementary Table S1. Comparison of the median abundance of the genera showed that no two microbiomes were identical (Figs. 6A–6C). In DM1, 12 genera decreased and 12 genera increased in abundance compared with CT1 (Table 3). Further, in DM2, four and seven genera decreased and increased, respectively, in abundance compared with CT2 (Table 4). In contrast, the DRC1 and DRC2 microbiomes showed many similarities. For example, eight identical genera showed a decrease and three identical genera showed an increase in abundance in both DRC1 and DRC2 microbiomes compared with their respective controls (Tables 56). Box plots also confirmed a significant difference in abundance in genera in DM and DRC compared with their respective controls (Supplementary Figs. S1, S2Tables 36). 
Figure 6.
 
(A) Abundance (%) of genera in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). (B) Median abundance of bacterial genera from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5).
Figure 6.
 
(A) Abundance (%) of genera in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). (B) Median abundance of bacterial genera from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5).
Table 3.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT1, n = 6) and Diabetic Rats (DM1, n = 6)
Table 3.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT1, n = 6) and Diabetic Rats (DM1, n = 6)
Table 4.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT2, n = 6) and Diabetic Rats (DM2, n = 6)
Table 4.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT2, n = 6) and Diabetic Rats (DM2, n = 6)
Table 5.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT3, n = 6) and Diabetic Rats Showing Retinal Changes (DRC1, n = 6)
Table 5.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT3, n = 6) and Diabetic Rats Showing Retinal Changes (DRC1, n = 6)
Table 6.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT4, n = 6) and Diabetic Rats Showing Retinal Changes (DRC2, n = 5)
Table 6.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT4, n = 6) and Diabetic Rats Showing Retinal Changes (DRC2, n = 5)
Heat-map analyses indicated that the microbiomes of CT1 and CT2 formed two subclades (a and b), whereas DM1 and DM2 together formed a distinct subclade c within which DM1 formed a subclade d, with the DM2 microbiomes dispersed on either side of subclade c (Fig. 7A). The CT3 and CT4 microbiomes also formed two subclades (e and f) (Fig. 7B). The majority of the DRC microbiomes, which included all of the DRC2 microbiomes and three DRC1 microbiomes, formed a subclade g distinct from CT3 and CT4 microbiomes; however, three DRC1 microbiomes formed a cluster with CT3 (subclade e) (Fig. 7B). Further, when all of the microbiomes were analyzed together by heat map, the majority of the CT microbiomes (17 of 24) formed subclade a, four formed subclade b, and the remaining three were in subclade c, which had five DM1 microbiomes. It is interesting that the majority of the DM and DRC microbiomes (20 of 23) did not group with subclade a, which had the majority of the control microbiomes. Five microbiomes of DRC2 and three of DRC1 formed a subclade d; the DM2 microbiomes appeared to be the most diverse and were distributed in subclades a, b, and c (Supplementary Fig. S3). The NMDS plot also indicated that the CT microbiomes formed one distinct cluster (green), separate from both the DM (red) and the DRC (purple) groups (P < 0.05). Further, the majority of DM and DRC microbiomes also showed separation at the OTU level (Figs. 8A, 8B). 
Figure 7.
 
Two-dimensional heat map representing rank-normalized abundances (scaled between 0 and 1) of 20 differentially abundant bacterial genera (median abundance of >0.1% in at least one group) for (A) the gut bacterial microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6), and (B) the gut bacterial microbiomes of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). The discriminating genera are arranged along two dimensions (axes) based on hierarchical clustering.
Figure 7.
 
Two-dimensional heat map representing rank-normalized abundances (scaled between 0 and 1) of 20 differentially abundant bacterial genera (median abundance of >0.1% in at least one group) for (A) the gut bacterial microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6), and (B) the gut bacterial microbiomes of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). The discriminating genera are arranged along two dimensions (axes) based on hierarchical clustering.
Figure 8.
 
NMDS plots depicting β-diversity using Bray–Curtis dissimilarity in microbiomes at the OTU level for (A) control rats (CT1–CT4, n = 24, green), diabetic rats (DM1 and DM2, n = 12, red), and diabetic rats showing retinal changes (DRC1 and DRC2, n = 11, purple) and (B) diabetic rats (DM1 and DM2, n = 12) versus diabetic rats showing retinal changes (DRC1 and DRC2, n = 11).
Figure 8.
 
NMDS plots depicting β-diversity using Bray–Curtis dissimilarity in microbiomes at the OTU level for (A) control rats (CT1–CT4, n = 24, green), diabetic rats (DM1 and DM2, n = 12, red), and diabetic rats showing retinal changes (DRC1 and DRC2, n = 11, purple) and (B) diabetic rats (DM1 and DM2, n = 12) versus diabetic rats showing retinal changes (DRC1 and DRC2, n = 11).
Interactions Among the Gut Bacterial Genera of Control, DM, and DRC Rats
The gut bacterial microbiomes of CT, DM, and DRC rats (Supplementary Fig. S4) were connected through several “hub” genera or nodes (with a high degree of interaction >10) interacting either positively or negatively with other genera. The CT group with 11 hub genera shared one genera (Ruminococcaceae;g) with DM and seven (Bradyrhizobium, Vibrio, Sphingomonas, Brevundimonas, Methylobacterium, Ralstonia, and Akkermansia) with DRC, and the remaining three (Oribacterium, Treponema 2, and Sutterella) were unique to the CT group. No hub genera were shared between the DM and DRC microbiomes; the hub genera Methanosphaera, Ruminiclostridium, and Subdoligranulum were unique to the DM group, and the hub genera Poterioochromonas spp. DS, Siccibacter, Coriobacteriaceae;g_UCG003, Slackia, Libanicoccus, Pseudomonas, and Enorma were unique to the DRC group. 
Discussion
Animal Model and Novelty of the Study
This study confirms that STZ-induced diabetic rats, 3 months after the induction of diabetes, exhibited changes in the retina, such as decreased retinal thickness, increased expression of HIF-1α and VEGF, and decreased rhodopsin expression (Figs. 23), thus confirming earlier observations regarding the occurrence of DR 3 to 6 months after development of DM in STZ-induced DM rats.2123,26,27 A few studies also monitored gut microbiome changes in STZ-induced rats after varying periods of time from 1 week to 15 weeks2832 after the induction of DM. But, the novelty of this study compared with the above studies includes the following: (1) Microbiome dynamics were monitored in STZ-induced diabetic rats for 4 months at regular monthly intervals, unlike earlier studies,2832 which monitored microbiome changes only at a single time point (e.g., 1 week, 30 days, 15 weeks). The first 2 months correspond with induction and progression of diabetes, whereas the third and fourth months coincide with the occurrence of retinal changes. (2) Zhou et al.32 monitored Zucker diabetic fatty (ZDF) rats for 15 weeks, but these rats exhibit hyperglycemia at 8 weeks of age32; thus, changes in the microbiome were monitored at only 8 weeks32 after the occurrence of diabetes compared with the 16 weeks monitored by us. (3) ZDF rats differ from STZ-induced diabetes rats in that the former has a mutated leptin receptor. (4) None of the studies simultaneously monitored retinal marker changes and gut microbiome changes, as was done in this study from 1 month to 4 months. 
Gut Bacterial Microbiome in the Control Rats
In the current study, 10 abundant phyla were detected in the gut microbiomes of the control rats (Table 1), in accordance with earlier reports.33,34 Further, at the genera level, 18 abundant genera were identified in the gut microbiome of the rats, and 11 of these genera had been reported earlier by Nagpal et al.26 (Supplementary Table S1). The observed differences in the microbiome could be due to diet differences35 or the influence of caging and bedding.36 Longitudinal changes in abundance at the phyla level were also observed in the control, DM, and DRC microbiomes (Figs. 5A–5C). 
Dysbiosis in Diabetic Rats
In agreement with earlier reports Firmicutes and Bacteroidetes33,34 were the most abundant phyla and the ratio of Firmicutes to Bacteroidetes increased marginally only when DM1 was compared with CT1. This was not unexpected, as an increased ratio of Firmicutes to Bacteroidetes is known to be associated with inflammation, obesity, and insulin resistance.28,37 
At the genera level in the DM1 and DM2 rats, a decrease in the abundance of Ruminoclostridium, Oscillibacter, Rikenellaceae;g_RC9, Rikenellaceae;g_dgA-11, Bacteroides, Alistipes, Eubacterium, and Anaerofilum was observed (Tables 34). These bacteria are known to be anti-inflammatory,36 reduce bacterial translocation across the intestine, maintain gut integrity,36 and are associated with β-cell autoimmunity and insulin resistance.38 Eventually, a decrease in their abundance is likely to be associated with DM, thus confirming earlier studies in diabetic rats and in T2DM subjects.17,28,39 Other genera that decreased in abundance in DM rats could be implicated in several other physiological aspects, such as Parasutterella in bile acid maintenance and cholesterol metabolism, Tyzzerella in non-alcoholic fatty liver disease,40 Anaerovorax in inflammatory bowel syndrome, and Rikenella and genus Rikenellaceae;g_RC9 in high-fat diets and lipid metabolism. 
In addition to the above genera that decreased in abundance, 18 genera increased in DM1 and DM2 gut microbiomes (Tables 34). These included eight pathogens, one proinflammatory bacteria, six anti-inflammatory/probiotic bacteria,33,42,43 and three others. An increase in Holdemanella, for example, has been implicated in constipation and chronic kidney disease,44,45 Faecalibacterium in inflammatory bowel syndrome, and Oribacterium in periodontal disease.46 The increase in Coriobacteriaceae;g_UCG-003 in DM1 rats could be related to the beneficial effects of Roux-en-Y gastric bypass on type 2 diabetes.47 
Gut microbiome dysbiosis has also been reported in people with diabetes17 and in diabetic mice.20 These diabetic mice (db/db), following intermittent fasting, showed a reduction in DR end points; thus, it was suggested that restructuring of the gut microbiome by intermittent fasting prevents retinopathy in db/db mice.20 Other studies on mice have dealt with the effects of various antidiabetic drugs on the gut microbiome and were not considered for comparison in the current study.33 
Gut Microbiome Longitudinal Changes in Diabetic Rats
Abundance changes in the DM2 rats compared with the DM1 rats (Tables 34) at the genera level were not identical. Only two genera, Rikenellaceae;g_dgA-11, which has anti-inflammatory properties, and Parasutterella decreased in DM1 and DM2. A decrease in Parasutterella would be detrimental to DM subjects because Parasutterella has been correlated with reductions in low-density lipoprotein levels.48 Further, Coriabacteriaceae;g_UCG-003 was the only genus that increased in both DM1 and DM2 microbiomes, and four other genera (Phascolarctobacterium, Lachnospiraceae, Megasphaera, and Romboutsia) that are either probiotic or anti-inflammatory in function increased only in DM2 microbiomes.42,43 
Gut Microbiome Changes in Diabetic Rats With Retinal Changes
In DRC1 and DRC2 rats, the majority of the genera that decreased in abundance compared with CT3 and CT4 rats possess anti-inflammatory (producers of short-chain fatty acids) or probiotic properties (Tables 5, 6). These genera are associated with other diseases, including the Ruminococcaceae group and Ruminococcaceae;g_DTU089 in Crohn's disease49; Anaerofilum in increased intestinal permeability50; Erysipelotrichaceae in inflammation of gastrointestinal tract51; Intestinimonas in anti-inflammation52; Pygmaiobacter in hypertension53; and Rikenellaceae;g_dgA-11 in the maturation and development of gut microbiota54 (Tables 5, 6). Several other bacteria that increased in abundance in DRC rats were either proinflammatory or pathogenic. The proinflammatory bacteria included the Erysipelotrichaceae group (earlier found to be involved in gastrointestinal tract disorders, colorectal cancer, and tumorogenic animals)51 and Turicibacter. There were several pathogenic bacteria (Tables 5, 6), but Treponema was the only pathogen common to human and rat DRC gut microbiomes.17 In DRC1 and DRC2, pathogens such as Methanobrevibacter and Tyzrella decreased. Earlier studies had indicated that Tyzrella was over-represented in Crohn's disease49 and is associated with non-alcoholic fatty liver disease in rats.41 Butyricimonas, a proinflammatory bacterium, also decreased in DRC1, and a decrease in Butyricimonas has been reported in T1DM patients.55 Bacteroides, a probiotic, also decreased in DRC1. The relevance of the observed increase in Firmicutes bacterium CAG:822 and the gut bacterium Libanicoccus54 in DM1 is not known. 
Network and β-Diversity Analysis
Network analyses indicated that the gut bacterial microbiomes of the CT, DM, and DRC group rats were different. Further, β-diversity analysis (Fig. 8) also confirmed that the CT microbiomes were distinct from the DM and DRC groups, but the DM and DRC microbiomes showed an overlap (Fig. 8). Overall, the microbiomes of DM and DRC rats are distinct from those of the control microbiomes, and functionally the trend appears to be an increase in proinflammatory and pathogenic bacteria and a decrease in anti-inflammatory and probiotic bacteria in DM and DRC microbiomes. Dysfunction of the intestinal barrier, leading to enhanced permeability and translocation of microbial molecules into the systemic circulation, has been implicated in DM56,57 obesity and hyperglycemia and increased risk of systemic infection.57 Similar studies on the molecular and cellular orchestrators of diabetic retinopathy are lacking. 
Study Limitations
Fecal pellets as the DNA source may not be representative of the entire gut microbiome. Also, metabolomics of blood samples could have revealed microbial metabolites that may have influenced the DRC rats. 
Conclusions
We confirmed dysbiosis in the gut microbiomes of rats with DM at the phylum and genus levels compared with CT rats. 
We also observed dysbiosis in the gut microbiomes of rats with DRC at the phylum and genus levels compared with CT rats. We observed a decrease in anti-inflammatory and probiotic bacteria and an increase in proinflammatory and pathogenic bacteria in the DRC rats. 
Additional studies based on fecal microbial transplantation would help establish the connection between the gut microbiome and DRC unequivocally. 
Future searches for novel therapeutics for people with DM and DR could benefit from the current information. 
Acknowledgments
Supported by the Prof Brien Holden Eye Research Centre and Hyderabad Eye Research Foundation, L. V. Prasad Eye Institute, and by a grant from the Department of Science and Technology, Government of India (EMR/2017/000815). The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. 
Disclosure: S.R. Padakandla, None; T. Das, None; G. Sai Prashanthi, None; K.K. Angadi, None; S.S. Reddy, None; G.B. Reddy, None; S. Shivaji, None 
References
Nørgaard MF, Grauslund J. Automated screening for diabetic retinopathy - a systematic review. Ophthalmic Res. 2018; 60: 9–17. [CrossRef] [PubMed]
Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019; 157: 107843. [CrossRef] [PubMed]
International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.
Fernandes R, Viana SD, Nunes S, Reis F. Diabetic gut microbiota dysbiosis as an inflammaging and immunosenescence condition that fosters progression of retinopathy and nephropathy. Biochim Biophys Acta Mol Basis Dis. 2019; 1865: 1876–1897. [CrossRef] [PubMed]
Romero-Aroca P, Navarro-Gil R, Valls-Mateu A, Sagarra-Alamo R, Moreno-Ribas A, Soler N. Differences in incidence of diabetic retinopathy between type 1 and 2 diabetes mellitus: a nine-year follow-up study. Br J Ophthalmol. 2017; 101: 1346–1351. [CrossRef] [PubMed]
Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010; 376: 124–136. [CrossRef] [PubMed]
Song SJ, Wong TY. Current concepts in diabetic retinopathy. Diabetes Metab J. 2014; 38: 416–425. [CrossRef] [PubMed]
Kulkarni S, Kondalkar S, Mactaggart I, et al. Estimating the magnitude of diabetes mellitus and diabetic retinopathy in an older age urban population in Pune, western India. BMJ Open Ophthalmol. 2019; 4: e000201. [CrossRef] [PubMed]
Arumugam M, Raes J, Pelletier E, et al. Enterotypes of the human gut microbiome. Nature. 2011; 473: 174–180. [CrossRef] [PubMed]
Tlaskalová-Hogenová H, Štěpánková R, Kozáková H, et al. The role of gut microbiota (commensal bacteria) and the mucosal barrier in the pathogenesis of inflammatory and autoimmune diseases and cancer: contribution of germ-free and gnotobiotic animal models of human diseases. Cell Mol Immunol. 2011; 8: 110–120. [CrossRef] [PubMed]
Zackular JP, Baxter NT, Iverson KD, et al. The gut microbiome modulates colon tumorigenesis. mBio. 2013; 4: e00692–e00713. [CrossRef] [PubMed]
Malan-Muller S, Valles-Colomer M, Raes J, Lowry CA, Seedat S, Hemmings SMJ. The gut microbiome and mental health: implications for anxiety- and trauma-related disorders. Omics. 2018; 22: 90–107. [CrossRef] [PubMed]
Jayasudha R, Chakravarthy SK, Prashanthi GS, et al. Alterations in gut bacterial and fungal microbiomes are associated with bacterial keratitis, an inflammatory disease of the human eye. J Biosci. 2018; 43: 835–856. [CrossRef] [PubMed]
Kalyana Chakravarthy S, Jayasudha R, Ranjith K, et al. Alterations in the gut bacterial microbiome in fungal keratitis patients. PLoS One. 2018a; 13: e0199640. [CrossRef]
Kalyana Chakravarthy S, Jayasudha R, Sai Prashanthi G, et al. Dysbiosis in the gut bacterial microbiome of patients with uveitis, an inflammatory disease of the eye. Indian J Microbiol. 2018b; 58: 457–469. [CrossRef]
Jayasudha R, Das T, Kalyana Chakravarthy S, et al. Gut mycobiomes are altered in people with type 2 diabetes mellitus and diabetic retinopathy. PLoS One. 2020; 15: e0243077. [CrossRef] [PubMed]
Das T, Jayasudha R, Chakravarthy S, et al. Alterations in the gut bacterial microbiome in people with type 2 diabetes mellitus and diabetic retinopathy. Sci Rep. 2021; 11: 2738. [CrossRef] [PubMed]
Lai AK, Lo AC. Animal models of diabetic retinopathy: summary and comparison. J Diabetes Res. 2013; 2013: 106594. [CrossRef] [PubMed]
Olivares AM, Althoff K, Chen GF, et al. Animal models of diabetic retinopathy. Curr Diab Rep. 2017; 17: 93. [CrossRef] [PubMed]
Beli E, Yan Y, Moldovan L, et al. Restructuring of the gut microbiome by intermittent fasting prevents retinopathy and prolongs survival in db/db mice. Diabetes. 2018; 67: 1867–1879. [CrossRef] [PubMed]
Shruthi K, Reddy SS, Reddy GB. Ubiquitin-proteasome system and ER stress in the retina of diabetic rats. Arch Biochem Biophys. 2017; 627: 10–20. [CrossRef] [PubMed]
Raghu G, Akileshwari C, Reddy VS, Reddy GB. Attenuation of diabetic retinopathy in rats by ellagic acid through inhibition of AGE formation. J Food Sci Technol. 2017; 54: 2411–2421. [CrossRef] [PubMed]
Reddy VS, Raghu G, Reddy SS, Pasupulati AK, Suryanarayana P, Reddy GB. Response of small heat shock proteins in diabetic rat retina. Invest Ophthalmol Vis Sci. 2013; 54: 7674–7682. [CrossRef] [PubMed]
Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with the Folin phenol reagent. J Biol Chem. 1951; 193: 265–275. [CrossRef] [PubMed]
Shivaji S, Jayasudha R, Chakravarthy SK, et al. Alterations in the conjunctival surface bacterial microbiome in bacterial keratitis patients. Exp Eye Res. 2021; 203: 108418. [CrossRef] [PubMed]
Nagpal R, Wang S, Solberg Woods LC, et al. Comparative microbiome signatures and short-chain fatty acids in mouse, rat, non-human primate, and human feces. Front Microbiol. 2018; 9: 2897. [CrossRef] [PubMed]
Gong C-Y, Lu B, Hu Q-W, Ji L-L. Streptozotocin induced diabetic retinopathy in rat and the expression of vascular endothelial growth factor and its receptor. Int J Ophthalmol. 2013; 6: 573–577. [PubMed]
Ma Q, Li Y, Wang J, et al. Investigation of gut microbiome changes in type 1 diabetic mellitus rats based on high-throughput sequencing. Biomed Pharmacother. 2020; 124: 109873. [CrossRef] [PubMed]
Xue M, Liu Y, Xu H, et al. Propolis modulates the gut microbiota and improves the intestinal mucosal barrier function in diabetic rats. Biomed Pharmacother. 2019; 118: 109393. [CrossRef] [PubMed]
Sohail MU, Shabbir MZ, Steiner JM,, et al. Molecular analysis of the gut microbiome of diabetic rats supplemented with prebiotic, probiotic, and synbiotic foods. Int J Diabetes Dev Ctries. 2017; 37: 419–425. [CrossRef]
Zhang Q, Xiao X, Zheng J, et al. Featured article: structure moderation of gut microbiota in liraglutide-treated diabetic male rats. Exp Biol Med (Maywood). 2018; 243(1): 34–44. [CrossRef] [PubMed]
Zhou W, Xu H, Zhan L, Lu X, Zhang L. Dynamic development of fecal microbiome during the progression of diabetes mellitus in Zucker diabetic fatty rats. Front Microbiol. 2019; 10: 232. [CrossRef] [PubMed]
Liu S, Si C, Yu Y, et al. Multi-omics analysis of gut microbiota and metabolites in rats with irritable bowel syndrome. Front Cell Infect Microbiol. 2019; 9: 178. [CrossRef] [PubMed]
Tomas J, Langella P, Cherbuy C. The intestinal microbiota in the rat model: major breakthroughs from new technologies. Anim Health Res Rev. 2012; 13: 54–63. [CrossRef] [PubMed]
Singh RK, Chang HW, Yan D,, et al. Influence of diet on the gut microbiome and implications for human health. J Transl Med. 2017; 15: 73. [CrossRef] [PubMed]
Ericsson AC, Gagliardi J, Bouhan D, Spollen WG, Givan SA, Franklin CL. The influence of caging, bedding, and diet on the composition of the microbiota in different regions of the mouse gut. Sci Rep. 2018; 8: 4065. [CrossRef] [PubMed]
Zhao L. The gut microbiota and obesity: from correlation to causality. Nat Rev Microbiol. 2013; 11: 639–647. [CrossRef] [PubMed]
Van den Abbeele P, Belzer C, Goossens M, et al. Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J. 2013; 7: 949–961. [CrossRef] [PubMed]
Bibbò S, Dore MP, Pes GM, Delitala G, Delitala AP. Is there a role for gut microbiota in type 1 diabetes pathogenesis? Ann Med. 2017; 49: 11–22. [CrossRef] [PubMed]
Forslund K, Hildebrand F, Nielsen T, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015; 528: 262–266. [CrossRef] [PubMed]
Tang W, Yao X, Xia F, et al. Modulation of the gut microbiota in rats by Hugan Qingzhi tablets during the treatment of high-fat-diet-induced nonalcoholic fatty liver disease. Oxid Med Cell Longev. 2018; 2018: 7261619. [PubMed]
Wu F, Guo X, Zhang J, Zhang M, Ou Z, Peng Y. Phascolarctobacterium faecium abundant colonization in human gastrointestinal tract. Exp Ther Med. 2017; 14: 3122–3126. [CrossRef] [PubMed]
Gerritsen J, Hornung B, Renckens B, et al. Genomic and functional analysis of Romboutsia ilealis CRIBT reveals adaptation to the small intestine. PeerJ. 2017; 5: e3698. [CrossRef] [PubMed]
Lun H, Yang W, Zhao S, et al. Altered gut microbiota and microbial biomarkers associated with chronic kidney disease. Microbiologyopen. 2019; 8: e00678. [CrossRef] [PubMed]
Huang LS, Kong C, Gao RY, et al. Analysis of fecal microbiota in patients with functional constipation undergoing treatment with synbiotics. Eur J Clin Microbiol Infect Dis. 2018; 37: 555–563. [CrossRef] [PubMed]
Lourenςo TGB, Spencer SJ, Alm EJ, Colombo APV. Defining the gut microbiota in individuals with periodontal diseases: an exploratory study. J Oral Microbiol. 2018; 10: 1487741. [CrossRef] [PubMed]
Liu H, Zhang H, Wang X, Yu X, Hu C, Zhang X. The family Coriobacteriaceae is a potential contributor to the beneficial effects of Roux-en-Y gastric bypass on type 2 diabetes. Surg Obes Relat Dis. 2018; 14: 584–593. [CrossRef] [PubMed]
Bush JR, Alfa MJ. Increasing levels of Parasutterella in the gut microbiome correlate with improving low-density lipoprotein levels in healthy adults consuming resistant potato starch during a randomised trial. BMC Nutr. 2020; 6: 72. [CrossRef] [PubMed]
Knip M, Siljander H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nat Rev Endocrinol. 2016; 12: 154–167. [CrossRef] [PubMed]
Leclercq S, Matamoros S, Cani PD, et al. Intestinal permeability, gut-bacterial dysbiosis, and behavioral markers of alcohol-dependence severity. Proc Natl Acad Sci U S A. 2014; 111: E4485–E4493. [CrossRef] [PubMed]
Chen W, Liu F, Ling Z, Tong X, Xiang C. Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer. PLoS One. 2012; 7: e39743. [CrossRef] [PubMed]
Bui TP, Shetty SA, Lagkouvardos I, et al. Comparative genomics and physiology of the butyrate-producing bacterium Intestinimonas butyriciproducens. Environ Microbiol Rep. 2016; 8: 1024–1037. [CrossRef] [PubMed]
Calderón-Pérez L, Gosalbes MJ, Yuste S, et al. Gut metagenomic and short chain fatty acids signature in hypertension: a cross-sectional study. Sci Rep. 2020; 10: 6436. [CrossRef] [PubMed]
Wei Z-Y, Rao J-H, Tang M-T, et al. Characterization of dynamic age-dependent changes and driver microbes in primate gut microbiota during host's development and healthy aging via captive crab-eating macaque model. bioRxiv, https://doi.org/10.1101/2020.03.30.015305.
Alkanani AK, Hara N, Gottlieb PA, et al. Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Diabetes. 2015; 64: 3510–3520. [CrossRef] [PubMed]
Falagas ME, Kompoti M. Obesity and infection. Lancet Infect Dis. 2006; 6: 438–446. [CrossRef] [PubMed]
Thaiss CA, Levy M, Grosheva I, et al. Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science. 2018; 359: 1376–1383. [CrossRef] [PubMed]
Konikoff T, Gophna U. Oscillospira: a central, enigmatic component of the human gut microbiota. Trends Microbiol. 2016; 24(7): 523–524. [CrossRef] [PubMed]
Loh G, Blaut M. Role of commensal gut bacteria in inflammatory bowel diseases. Gut Microbes. 2012; 3(6): 544–555. [CrossRef] [PubMed]
Sogodogo E, Drancourt M, Grine G. Methanogens as emerging pathogens in anaerobic abscesses. Eur J Clin Microbiol Infect Dis. 2019; 38(5): 811–818. [CrossRef] [PubMed]
Figure 1.
 
Average blood sugar levels of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2).
Figure 1.
 
Average blood sugar levels of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2).
Figure 2.
 
Hematoxylin and eosin–stained retinal sections of control (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). Scale bar: 20 µm. GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; PRL, photoreceptor layer.
Figure 2.
 
Hematoxylin and eosin–stained retinal sections of control (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). Scale bar: 20 µm. GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; PRL, photoreceptor layer.
Figure 3.
 
(AC) Quantitative expression of HIF-1α by immunoblotting (A) and VEGF (B) and rhodopsin (C) by immunohistochemistry using specific antibodies in the retina of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). In A, levels of HIF-1α were calculated using tubulin as a control, and the data (mean ± SEM of three independent experiments) are represented as percentage of expression compared with control rats. In B and C, VEGF- and rhodopsin-specific antibodies were used for immunohistochemical staining. Arrows indicate either the increased production of VEGF or decreased production of rhodopsin. Scale bar: 20 µm. P < 0.05 was considered significant.
Figure 3.
 
(AC) Quantitative expression of HIF-1α by immunoblotting (A) and VEGF (B) and rhodopsin (C) by immunohistochemistry using specific antibodies in the retina of control rats (CT1–CT4), diabetic rats (DM1 and DM2), and diabetic rats showing retinal changes (DRC1 and DRC2). In A, levels of HIF-1α were calculated using tubulin as a control, and the data (mean ± SEM of three independent experiments) are represented as percentage of expression compared with control rats. In B and C, VEGF- and rhodopsin-specific antibodies were used for immunohistochemical staining. Arrows indicate either the increased production of VEGF or decreased production of rhodopsin. Scale bar: 20 µm. P < 0.05 was considered significant.
Figure 4.
 
Rarefaction curves and (B) alpha diversity indices (Shannon diversity index, Simpson index, number of observed OTUs, and Chao1 index) of the gut bacterial microbiomes of control rats (CT, n = 24), diabetic rats (DM, n = 12), and diabetic rats showing retinal changes (DRC, n = 11). Asterisks indicate significant differences between CT and DM rats (*) and between CT and DRC rats (**), as determined by Student's t-test (P < 0.05).
Figure 4.
 
Rarefaction curves and (B) alpha diversity indices (Shannon diversity index, Simpson index, number of observed OTUs, and Chao1 index) of the gut bacterial microbiomes of control rats (CT, n = 24), diabetic rats (DM, n = 12), and diabetic rats showing retinal changes (DRC, n = 11). Asterisks indicate significant differences between CT and DM rats (*) and between CT and DRC rats (**), as determined by Student's t-test (P < 0.05).
Figure 5.
 
(A) Abundance (%) of phyla in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). (B) Median abundance of bacterial phyla from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). CT1, CT2, CT3, and CT4 refer to control rats after 1, 2, 3, and 4 months, respectively; DM1 and DM2 refer to rats after 1 and 2 months, respectively, of STZ treatment; and DRC1 and DRC2 refer to rats after 1 and 2 months, respectively, of detection of retinal changes.
Figure 5.
 
(A) Abundance (%) of phyla in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). (B) Median abundance of bacterial phyla from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2, n = 5). CT1, CT2, CT3, and CT4 refer to control rats after 1, 2, 3, and 4 months, respectively; DM1 and DM2 refer to rats after 1 and 2 months, respectively, of STZ treatment; and DRC1 and DRC2 refer to rats after 1 and 2 months, respectively, of detection of retinal changes.
Figure 6.
 
(A) Abundance (%) of genera in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). (B) Median abundance of bacterial genera from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5).
Figure 6.
 
(A) Abundance (%) of genera in the gut microbiomes of control rats (CT1, n = 6; CT2, n = 6; CT3, n = 6; CT4, n = 6), diabetic rats (DM1, n = 6; DM2, n = 6), and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). (B) Median abundance of bacterial genera from gut microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6). (C) Median abundance of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5).
Figure 7.
 
Two-dimensional heat map representing rank-normalized abundances (scaled between 0 and 1) of 20 differentially abundant bacterial genera (median abundance of >0.1% in at least one group) for (A) the gut bacterial microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6), and (B) the gut bacterial microbiomes of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). The discriminating genera are arranged along two dimensions (axes) based on hierarchical clustering.
Figure 7.
 
Two-dimensional heat map representing rank-normalized abundances (scaled between 0 and 1) of 20 differentially abundant bacterial genera (median abundance of >0.1% in at least one group) for (A) the gut bacterial microbiomes of control rats (CT1, n = 6; CT2, n = 6) and diabetic rats (DM1, n = 6; DM2, n = 6), and (B) the gut bacterial microbiomes of control rats (CT3, n = 6; CT4, n = 6) and diabetic rats showing retinal changes (DRC1, n = 6; DRC2 n = 5). The discriminating genera are arranged along two dimensions (axes) based on hierarchical clustering.
Figure 8.
 
NMDS plots depicting β-diversity using Bray–Curtis dissimilarity in microbiomes at the OTU level for (A) control rats (CT1–CT4, n = 24, green), diabetic rats (DM1 and DM2, n = 12, red), and diabetic rats showing retinal changes (DRC1 and DRC2, n = 11, purple) and (B) diabetic rats (DM1 and DM2, n = 12) versus diabetic rats showing retinal changes (DRC1 and DRC2, n = 11).
Figure 8.
 
NMDS plots depicting β-diversity using Bray–Curtis dissimilarity in microbiomes at the OTU level for (A) control rats (CT1–CT4, n = 24, green), diabetic rats (DM1 and DM2, n = 12, red), and diabetic rats showing retinal changes (DRC1 and DRC2, n = 11, purple) and (B) diabetic rats (DM1 and DM2, n = 12) versus diabetic rats showing retinal changes (DRC1 and DRC2, n = 11).
Table 1.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 1 Month (CT1, n = 6) and 2 Months (CT2, n = 6) and Diabetic Rats After 1 Month (DM1, n = 6) and 2 Months (DM2, n = 6)
Table 1.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 1 Month (CT1, n = 6) and 2 Months (CT2, n = 6) and Diabetic Rats After 1 Month (DM1, n = 6) and 2 Months (DM2, n = 6)
Table 2.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 3 Months (CT3, n = 6) and 4 Months (CT4, n = 6) and Diabetic Rats Showing Retinal Changes After 1 Month (DRC1, n = 6) and 2 Months (DRC2, n = 6)
Table 2.
 
Median Abundance (%) of Phyla in Gut Bacterial Microbiomes of Control Rats After 3 Months (CT3, n = 6) and 4 Months (CT4, n = 6) and Diabetic Rats Showing Retinal Changes After 1 Month (DRC1, n = 6) and 2 Months (DRC2, n = 6)
Table 3.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT1, n = 6) and Diabetic Rats (DM1, n = 6)
Table 3.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT1, n = 6) and Diabetic Rats (DM1, n = 6)
Table 4.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT2, n = 6) and Diabetic Rats (DM2, n = 6)
Table 4.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT2, n = 6) and Diabetic Rats (DM2, n = 6)
Table 5.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT3, n = 6) and Diabetic Rats Showing Retinal Changes (DRC1, n = 6)
Table 5.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT3, n = 6) and Diabetic Rats Showing Retinal Changes (DRC1, n = 6)
Table 6.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT4, n = 6) and Diabetic Rats Showing Retinal Changes (DRC2, n = 5)
Table 6.
 
Median Abundance (>0.05% in Any One of the Groups) of Discriminatory Genera (P ≤ 0.05) in Gut Bacterial Microbiomes From Control Rats (CT4, n = 6) and Diabetic Rats Showing Retinal Changes (DRC2, n = 5)
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