Microbiome in Chronic Kidney Disease (CKD): An Omics Perspective
Abstract
:1. Introduction
- (i)
- symptoms of a kidney injury for more than 3 months at normal glomerular filtration rate (GFR),
- (ii)
- an increase in the urinary albumin:creatinine ratio (UACR) above 30 mg/g,
- (iii)
- (i)
- In 1978, researchers observed a significant alteration in gut microbiota in CKD patients in comparison to controls, and demonstrated microbiota’s ability to metabolize substrates causing uremic toxicity [26].
- (ii)
- Later on, another study indicated significant altered abundance of 175 bacterial operational taxonomic units (OTUs) in CKD and control rats [27]. An increase in Enterobacteriaceae/Proteobacteria and Bacteroidetes spp. with concomitant decrease in Prevotella, Bifidobacteriaceae and Lactobacilli spp. were suggested to intensify the sympathetic outflow causing hypertension and CKD. We may, therefore, hypothesize an impact of this microbiome on the renin-angiotensin-aldosterone-system (RAAS) that regulates sodium and water absorption in the kidney, thus directly controlling systemic blood pressure [28].
2. Transcriptomics in the Gut-Kidney-Axis
3. Proteomics in the Investigation of Gut-Kidney-Axis
3.1. Dietary Modifications to Improve Gut Health in CKD
3.2. Proteomics in the Heart-Gut-Kidney Axis
3.3. Proteomics in CKD Progression
4. Metabolomics in the Gut-Kidney-Axis
5. Expert Opinion
6. Future Perspective
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Reference | Organism | Design and Sample | Key Findings |
---|---|---|---|
Snelson [35] | Mice | HT* + RS* (n = 5) Vs C* (n = 5); HT (n = 5) in RNA extracted from Gut sections and Kidney cortex | systemic innate immune complement system: C3* and C5* effector molecules ↓ alagebrium inhibited the AGE pathway gut barrier integrity ↑ risk of kidney injury ↓ |
Reference | Organism | Design and Sample | Key Findings | Validation |
---|---|---|---|---|
Zybailov [42] | Rats | CKD-DS* (n = 9) Vs CKD-RS* (n = 9) in Frozen cecal content | Thioredoxin, S100-A6* ↑ serine-type endopeptidase inhibitor and metalloexopeptidase activities ↑ fibrolytic Ruminoccocus ↑ amylolytic Ruminoccocus ↓ | - |
Karaduta [44] | Mice | CKD (n = 4); CKD-RS (n = 4) Vs H* (n = 4); H-RS (n = 4) in Frozen cecal content | In CKD-RS: Indole mechanism ↓ butyrate-producing bacteria ↑ mucin-degrading bacteria ↓ IS* ↓ bacterial proliferation ↑ | - |
Lobel [49] | Mice | high-Saa* diet Vs low-Saa diet in Cecal content | Spp1*, Tgfb1*, Icam1*, Ccl2*, Timp1* ↓ Indole mechanism ↓ Escherichia coli (E. coli) ↑ tryptophanase (TnaA) ↓ | - |
Opdebeeck [50] | Rats | IS (n = 14) or pCS* (n = 14) Vs Vehicle (n = 14) in Aorta and Kidney tissue | In aorta: TGF-β and collagen I ↓ in IS GLUT1* ↓ in pCS No change in IL-1β*, klotho, TNF-α* and vascular cell adhesion molecule 1 → stress responsiveness pathway ↓ metabolic activity pathway ↓ calcium-associated processes ↓ → Glycoprotein 6 (GP6) reactivated on end of exposure | RT-PCR: GLUT1 -aorta. IL-1β, collagen I, klotho, TGF-β, TNF-α, and vascular cell adhesion molecule 1-left kidney. |
Smith [51] | Human | Endogenous CPP* Vs Synthetic CPP-I & -II in Serum samples |
| - |
Lin [52] | Mice | S-AKI* to CKD progression at day 2 and 7 in Kidney tissue (n = 18) | Hmgcs2*, S100-A8, Chil3* ↑ TNFα, Gsdmd*, caspase-1, ASC and ERK ↑ deregulation of mitochondrial inner membrane proteins Atp5j*, Ndufb1*, Cox2* | Immunoblot: Hmgcs2, S100-A8, Chil3, TNFα, Gsdmd, caspase-1 |
Reference | Organism | Design and Sample | Key Findings | Validation |
---|---|---|---|---|
Kanemitsu [54] | Mice | GF*-RF* (n = 5); SPF*-RF (n = 3) Vs GF (n = 4); SPF (n = 4) in Plasma, Fecal and Cecal contents | In GF-RF: renal inflammation ↑ renal fibrosis ↓ SCFAs*, IAA* ↓ n-3 type of polyunsaturated fatty acid ↓ | - |
Feng [55] | Rats | Study 1: CKD (n = 6) Vs Control (n = 6) in Serum, Colonic luminal contents, Colon tissues and Kidney tissues | In CKD: CCr* associated with polyamine metabolism & SBP* with glycine-conjugated metabolites. | |
Rats | Study 2: PAA-CKD (n = 8); PC-CKD (n = 8) Vs Controls (n = 8) in Serum, Colonic luminal contents, Colon tissues and Kidney tissues | In poricoic acid A (PAA) and Poria cocos (PC): microbial dysbiosis, hypertension and renal fibrosis ↓ ZO1*, occludin and claudin-1 ↑ IκB/NF-κB pathway ↓ Keap1/Nrf2 pathway ↑ Enterobacteriaceae, Sutterellaceae and Clostridiaceae_1 ↑ Clostridiaceae_2 and Leuconostocaceae ↓ | Western blot: ZO1, occludin and claudin-1 | |
Wang [56] | Human | Study 1: ESRD* (n = 223) Vs Healthy controls (n = 69) in Serum and Fecal samples | microbial derived uremic toxins ↑ uremic toxin precursors and secondary bile acids ↑ Eggerthella lenta, Flavonifractor spp. (mainly F. plautii), Alistipes spp. (mainly A. finegoldii and A. shahii), Ruminococcus spp. And Fusobacterium spp. ↑ Prevotella spp. (mainly P. copri), Clostridium spp. And several butyrate producers (Roseburia spp., Faecalibacterium prausnitzii and Eubacterium rectale) ↓ | |
Mice | Study 2: Transplantation of fecal metabolome from ESRD patients to GF-mice (n = 13) Vs Controls (n = 13) | In GF-ESRD mice: serum uremic toxins ↑ renal fibrosis and oxidative stress ↑ | Fecal microbiota transplantation from patient into GF-mice: serum uremic toxins ↑ renal fibrosis and oxidative stress ↑ | |
Rats | Study 3: Transplantation of fecal metabolome from ESRD patients to antibiotics treated rats (n = 9) Vs Controls (n = 9) in Serum and Fecal samples | In rats: Eggerthella lenta and Fusobacterium nucleatum increased production of uremic toxins Bifidobacterium animali reduced uremic toxins production | ||
Wu [32] | Human | CKD mild (stage 1 and 2, n = 26), moderate (stage 3, n = 26) and advanced (stage 4 and 5, n = 20) Vs Healthy controls (n = 20) in Fecal and Serum samples | Prevotella sp. 885, Weissella confuse, Roseburia faecis, and Bacteroides eggerthii ↓ Alloscardovia omnicolens, Merdibacter massiliensis, and Clostridium glycyrrhizinilyticum ↑ IS*, pCS*, hepatonic acid ↑ propionic acid, caproic acid ↓ | - |
Wu [57] | Human | CKD-LPD* (n = 16) Vs Healthy controls (n = 34); CKD-NPD* (n = 27) in Fecal and Serum samples | Anaerostipes and Eubacterium hallii ↑ Calditerricola, Streptococcus anginosus, Lactobacillus mucosa and Clostridium paraputrificum ↓ glyco λ-muricholic acid ↑ nonanoic acid ↓ D-alanine, ketone bodies and glutathione metabolism metabolites ↑ Butyrate producing bacteria (Lachnospiraceae and Bacteroidaceae families) ↓ | - |
Yenan Mo [58] | Rats | α-ketoacid + CKD (n = 8) Vs Control (n = 8); CKD (n = 8) in Fecal and Serum samples | tubular atrophy, glomerulosclerosis and gut fibrosis ↑ Methanobrevibacter, Akkermansia, Blautia and Anaerositipes ↑ Anaerovorax and Coprococcus_3 ↓ metabolites IS, betaine, choline and cholesterol ↓ metabolites PAGly*, PAGln*, and pCS ↑ → α-ketoacid exhibits a reno-protective in CKD rats | - |
Nanto-Hara [61] | Mice | CKD-linaclotide (n = 6) Vs CKD (n = 5) in Kidney tissue, Fecal and Plasma samples | collagen I, TGF-β*, Galectin-3 (Gal-3) and ST2 genes ↓ F4/80-positive macrophages in small intestine ↑ colonic claudin-1 ↓ → low dosage of linaclotide (10 µg/kg) decreased TMAO* → higher dosage of linaclotide (100 µg/kg) decreased BUN*, CCr, urea, trans-aconitate, TMAO, IS and Hippurate | qPCR: collagen I, TGF-β, Galectin-3 (Gal-3) and ST2 genes |
Zhang [62] | Rats | CKD-RC* (n = 6) Vs Normal (n = 6); CKD (n = 6); CKD-RR* (n = 6); CKD-CF* (n = 6) in Fecal samples | Ruminococcaceae UCG-014, Ruminococcus 1, Prevotellaceae_NK3B31_group, Lachnospiraceae NK4A136 group and Lachnospiraceae UCG-001 ↑ Desulfovibrio ↓ indole-3-propionic acid and phenylpyruvate ↓ tryptophan ↑ | - |
Saggi [63] | Human | CKD patients administered probiotic Renadyl™ (n = 24) in Plasma samples |
energy metabolism and regulation | - |
Bush [64] | Rats | Probenecid STN* (n = 5) Vs Probenecid control (n = 5) Vs STN (n = 5) in Plasma samples | In probenecid treated STN rats: 58 uremic toxins ↑ In probenecid treated control and STN rats: OAT(s)* mediated tubular secretion of organic anions ↓ → central role of OAT(s) transporters in the tubular secretion of various uremic toxins (IS, kynurenate and anthranilate) | - |
Kikuchi [65] | Rats | Study 1: Wild type DKD* (n = 4) Vs Transgenic overexpressing SLCO4C1* DKD (n = 4) in Plasma and Fecal samples | In wild type DKD (non-transgenic) rats: phenyl sulfate (PS) ↑ | |
Mice | Study 2: PS + db/db* mice (n = 6) Vs Control db/db mice (n = 5) in Plasma and Fecal samples | In PS administered db/db mice: TNF*-α, MCP-1* (Ccl2*), TGF-α1, Fn1* and collagen I (Col1a1) ↑ → elicit podocyte damage and increase glutathione levels | qPCR: TNF-α, MCP-1 (Ccl2), TGF-α1, Fn1 and collagen I (Col1a1) | |
Human | Study 3: DKD patients at t = 0 (n = 362) Vs DKD patients at t = 2 years (n = 362) in Plasma and Fecal samples | → PS predictive of the ACR levels, especially for microalbuminuria in DKD patients. | ||
Mice | Study 4: Pre-treatment** db/db mice (n = 10) Vs After-treatment ** db/db mice (n = 10) in Plasma and Fecal samples | On treatment with **2-aza-tyrosine: Coriobacteria and Erysipelotrichales ↓ | ||
Sun [66] | Human | Study 1: eGFRRD* patient (n = 10) Vs Healthy control (n = 10) at t = 0 and t = 1 year in Serum samples | In eGFRRD patients: IPA* ↓ at t = 0 and t = 1 year | - |
Human | Study 2: CKD patients (n = 140) Vs Healthy controls (n = 144) in Serum samples | In CKD patients: IS and pCS ↑ IPA ↓ → high levels of serum IPA may indicate a lower risk of rapid decline in their renal function and developing CKD. |
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Lohia, S.; Vlahou, A.; Zoidakis, J. Microbiome in Chronic Kidney Disease (CKD): An Omics Perspective. Toxins 2022, 14, 176. https://doi.org/10.3390/toxins14030176
Lohia S, Vlahou A, Zoidakis J. Microbiome in Chronic Kidney Disease (CKD): An Omics Perspective. Toxins. 2022; 14(3):176. https://doi.org/10.3390/toxins14030176
Chicago/Turabian StyleLohia, Sonnal, Antonia Vlahou, and Jerome Zoidakis. 2022. "Microbiome in Chronic Kidney Disease (CKD): An Omics Perspective" Toxins 14, no. 3: 176. https://doi.org/10.3390/toxins14030176