Advertisement

SN Comprehensive Clinical Medicine

, Volume 1, Issue 1, pp 23–25 | Cite as

Uddanam Kidney Nephropathy Under the Light of Metagenomics Perspective

  • P. V. Parvati Sai ArunEmail author
  • C. Obula Reddy
  • Yusuf Akhter
Medicine
  • 209 Downloads
Part of the following topical collections:
  1. Topical Collection on Medicine

Abstract

In this work, we proposed a research hypothesis which mainly focuses on the role of gut microbiota, in relation with chronic kidney disease, existing in the village of Uddanam belonging to the State of Andhra Pradesh, India. Earlier studies conducted on various physical and chemical parameters, such as food, water, soil and pesticides, could not find the exact cause of the aetiology of the disease. As there were no physical and chemical causative factors identified, this disease was hence named as mysterious Uddanam kidney nephropathy of unknown origins. There are few scientific pieces of evidence available in the literature, which suggests the role of gut microbiome dysbiosis and its impact on kidney disease. Such kinds of scientific studies which deal with the analysis of microbial communities of the gut region and identification of dysbiosis were not performed in the case of Uddanam kidney nephropathy. Thus, through this paper, we propose a hypothesis to analyse and establish the relationship between the gut dysbiosis and renal failure by performing the metagenomics study between the patients and the normal individuals. To the best of our knowledge, there are no reports available to date on the dysbiosis of the gut microbiome and its impact on Uddanam nephropathy and hence this research hypothesis is thus proposed.

Keywords

Uddanam region Kidney nephropathy Aetiology Andhra Pradesh India Metagenomics 

Introduction

The Uddanam region is geographically located in the south-eastern part of Srikakulam District of the Andhra Pradesh State in India. Recently, the Uddanam region has become very popular for its “Chronic kidney disease of unknown origin”. As of the year 2015–2016, it was estimated that about 34,000 people had this disease and about 4500 people died [1, 2]. The scientific literature and evidence related to this disease aetiology are very limited. To understand the aetiology of this chronic kidney disease (CKD), the physical and chemical causative factors such as soil, water, food, heat stress, pesticides and environmental samples were analysed by different research groups, but failed to provide the clues about the exact causative factors [2, 3, 4, 5, 6]. It was observed that the majority of people who are affected by this kidney disease in the Uddanam region are agriculture labourers [2]. In the year 2013, this CKD of the Uddanam region was named as Uddanam Nephropathy at the International Congress of Nephrology, China.

In the past, there are few studies which have reported about the relationship between the alteration of the gut microbiome and renal failure [7, 8, 9, 10, 11, 12, 13, 14, 15]. On the other hand, there are also few reports describe about the CKDs but does not establish any kind of relationship between physical, chemical and metabolic factors, responsible for the renal failure. Such type of CKDs were named as chronic kidney disease with an unknown origin (CKD-u) [16]. Uddanam kidney disease is also one of the CKDs which was considered as CKD-u [16]. As the analysis performed on the physical and chemical factors could not provide any clues about the exact cause of this CKD. In this report, we propose a hypothesis which may elucidate the relationship between alteration of the gut microbiome (Biological factor) and Uddanam CKD by the use of the concepts of metagenomics and its data analysis.

To proceed with the study, selection of correct participants and a collection of samples (blood and stools) have to be carried out according to the guidelines followed in the earlier reports [17, 18]. For applying the metagenomics approach, collection of fresh stools, their storage, DNA isolation, sequencing, pre-processing of the obtained data, assignment of operational taxonomic units, calculations of the abundance of bacterial families and their distributions between the patients and control samples have to be performed similarly as described in the previous reports [18].

By performing the comparative analysis on the metagenome data of the patients and control samples, we may clearly identify the dysbiosis between the patients and the normal individuals.

The basic assumption of our hypothesis is that in general at the time of birth human babies are sterile, without any contamination [19]. Later, the newborn is contaminated by bacteria due to the contact of the newborn with the vaginal fluids at the time of delivery. From that point of contact, the bacteria start colonising in different parts of the human body, such as the gastrointestinal tract (GIT) and mouth, and give rise to unique microbial communities [19]. The GIT of a healthy human being is mainly composed of more gram-negative bacteroidetes and low-GC firmicutes [20]. This “healthy gut microbiota” coevolves with the humans for their beneficial coexistence. In harsh conditions, when the healthy microbiome is exposed to various harsh environmental factors which include diet, toxins, drugs and also pathogens, then there is a change/alteration in composition and structure of healthy microbiota, termed as dysbiosis [7, 21].

Dysbiosis was proven to be one of the important causative factors for diseases such as obesity, type 2 diabetes, bowel disease, cardiovascular disease, cancer and asthma [20, 22, 23, 24, 25, 26, 27, 28, 29]. Now a days, there are an increasing number of evidences recorded, which state that due to dysbiosis there might be the progression of CKD and CKD-related problems [30, 31]. It was found that due to gut dysbiosis, the concentrations of the endotoxins are increased due to the processes such as fermentation carried out by pathogenic bacteria in the gut region [32]. As a result of such activities, the products which are formed due to these activities like endotoxins such as phenols, indoles and amines cross the intestinal barrier, and then mix with the bloodstream [32]. As there is an increase in the concentrations of some of the uremic toxins, it may lead to less production of reno-protective metabolites, which generally protect the kidneys [33]. Moreover, it was also reported that alteration in the composition of gut microbiome may lead to the circulation of high levels of lipopolysaccharides, which may further lead not only to immune de-regulation, but also may cause complete renal failure [34]. On the other hand, there are several reports suggesting the role of genetic polymorphisms in the genes APOL1 and MYH9 and their role in CKD. From the literature survey, we observed that the genes APOL1 and MYH9 co-segregate with each other [35]. However, it was proposed that the gene APOL1 is more intensely associated with CKD than the MYH9 gene [36, 37]. But to the best of our knowledge, these are reported from the different parts of the world, especially by analysing the data obtained from the populations of African, African-Americans, Brazilians and European-Americans [35, 38, 39, 40]. However, there are no reports existing in the literature describing the role of APOL1 or MYH9 genes or their genetic polymorphisms in CKD of Indian population [41, 42]. Thus, as there are strong evidence describing the negative correlation between the APOL1 and MYH9 gene involvement in CKD in Indian population, characterising the gut microbiome and identification of the dysbiosis in the microbial content would be a better option, since there is a strong positive correlation established between the gut dysbiosis and CKD from earlier studies.

Conclusion

In the case of Uddanam nephropathy, there are no studies reported to the date about the bacterial composition of healthy GIT and altered GIT of CKD patients (dysbiosis). Moreover, there is no documented evidence available about the comparison of normal healthy GIT microbiota and CKD-affected ones. After careful examination of the published literature, this hypothesis is proposed in the form of this report. This report is the first of its kind to provide research idea for the scientific community to resolve the kidney disease of Uddanam by the use of metagenomics and to elucidate the dysbiosis factors existing between the healthy and the patients. This will ultimately help the many individuals suffering from this disease.

Notes

Acknowledgements

The corresponding author thanks the Institute of Bioinformatics and Computational Biology (IBCB), Visakhapatnam, Andhra Pradesh, India, for its help in securing the news for local incidences of this study. The corresponding is also thankful for Chaitanya Bharathi Institute of Technology (CBIT), Gandipet, Hyderabad, Telangana for providing the facilities to communicate this manuscript.

Funding

Research in YA lab is funded by the Department of Biotechnology (Ministry of Science & Technology, Government of India) and Indian Council of Medical Research.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Abraham G, Varughese S, Thandavan T, Iyengar A, Fernando E, Naqvi S, et al. Chronic kidney disease hotspots in developing countries in South Asia. Clin Kidney J. 2016;9(1):135–41.CrossRefGoogle Scholar
  2. 2.
    Ganguli A. Uddanam nephropathy/regional nephropathy in India: preliminary findings and a plea for further research. Am J Kidney Dis. 2016;68(3):344–8.CrossRefGoogle Scholar
  3. 3.
    Reddy D, Gunasekar A. Chronic kidney disease in two coastal districts of Andhra Pradesh, India: role of drinking water. Environ Geochem Health. 2013;35(4):439–54.CrossRefGoogle Scholar
  4. 4.
    Satyanarayana G, Ramadasu P, Devi PP, Prasad N, Rao GN. Ground water quality assessment in Uddanam region, costal Srikakulam, Andhra Pradesh, India.Google Scholar
  5. 5.
    Raju TP, NG C, Srinivasu C, Ramanamam V, Ram SS, Sudarshan M, et al. Trace elemental analysis of soil samples of kidney effected area using EDXRF technique. International Journal of Scientific & Engineering Research. 2015;6(Issue):1472–9.Google Scholar
  6. 6.
    Gadde P, Sanikommu S, Manumanthu R, Akkaloori A. Uddanam nephropathy in India: a challenge for epidemiologists. Bull World Health Organ. 2017;95(12):848–9.  https://doi.org/10.2471/BLT.17.196758.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Al Khodor S, Shatat IF. Gut microbiome and kidney disease: a bidirectional relationship. Pediatr Nephrol. 2017;32(6):921–31.CrossRefGoogle Scholar
  8. 8.
    Armani R, Ramezani A, Yasir A, Sharama S, Canziani M, Raj D. Gut microbiome in chronic kidney disease. Curr Hypertens Rep. 2017;19(4):29.CrossRefGoogle Scholar
  9. 9.
    Kelsey R. Gut microbiome is unique in kidney stone disease. Nature Reviews Urology. 2016;13(7):368–9.CrossRefGoogle Scholar
  10. 10.
    Kieffer DA, Piccolo BD, Vaziri ND, Liu S, Lau WL, Khazaeli M, et al. Resistant starch alters gut microbiome and metabolomic profiles concurrent with amelioration of chronic kidney disease in rats. American Journal of Physiology-Renal Physiology. 2016;310(9):F857–F71.CrossRefGoogle Scholar
  11. 11.
    Lau WL, Vaziri ND. The leaky gut and altered microbiome in chronic kidney disease. J Ren Nutr. 2017;27(6):458–61.CrossRefGoogle Scholar
  12. 12.
    Nallu A, Sharma S, Ramezani A, Muralidharan J, Raj D. Gut microbiome in chronic kidney disease: challenges and opportunities. Transl Res. 2017;179:24–37.CrossRefGoogle Scholar
  13. 13.
    Ramezani A, Raj DS. The gut microbiome, kidney disease, and targeted interventions. Journal of the American Society of Nephrology. 2013:ASN. 2013080905.Google Scholar
  14. 14.
    Vasylyeva TL, Singh R. Gut microbiome and kidney disease in pediatrics: does connection exist? Front Microbiol. 2016;7:235.CrossRefGoogle Scholar
  15. 15.
    Wing MR, Patel SS, Ramezani A, Raj DS. Gut microbiome in chronic kidney disease. Exp Physiol. 2016;101(4):471–7.CrossRefGoogle Scholar
  16. 16.
    Weaver VM, Fadrowski JJ, Jaar BG. Global dimensions of chronic kidney disease of unknown etiology (CKDu): a modern era environmental and/or occupational nephropathy? BMC Nephrol. 2015;16(1):145.CrossRefGoogle Scholar
  17. 17.
    Levey AS, Coresh J, Bolton K, Culleton B, Harvey KS, Ikizler TA et al. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. American Journal of Kidney Diseases. 2002;39(2 SUPPL. 1).Google Scholar
  18. 18.
    Xu K-Y, Xia G-H, Lu J-Q, Chen M-X, Zhen X, Wang S, et al. Impaired renal function and dysbiosis of gut microbiota contribute to increased trimethylamine-N-oxide in chronic kidney disease patients. Sci Rep. 2017;7(1):1445.CrossRefGoogle Scholar
  19. 19.
    Dominguez-Bello MG, Costello EK, Contreras M, Magris M, Hidalgo G, Fierer N, et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc Natl Acad Sci. 2010;107(26):11971–5.CrossRefGoogle Scholar
  20. 20.
    Simenhoff M, Dunn S, Zollner G, Fitzpatrick M, Emery S, Sandine W, et al. Biomodulation of the toxic and nutritional effects of small bowel bacterial overgrowth in end-stage kidney disease using freeze-dried Lactobacillus acidophilus. Miner Electrolyte Metab. 1996;22(1–3):92–6.PubMedGoogle Scholar
  21. 21.
    Ursell LK, Clemente JC, Rideout JR, Gevers D, Caporaso JG, Knight R. The interpersonal and intrapersonal diversity of human-associated microbiota in key body sites. J Allergy Clin Immunol. 2012;129(5):1204–8.CrossRefGoogle Scholar
  22. 22.
    Al Khodor S, Reichert B, Shatat IF. The microbiome and blood pressure: can microbes regulate our blood pressure? Fron Ped. 2017;5:138.CrossRefGoogle Scholar
  23. 23.
    Frank DN, Amand ALS, Feldman RA, Boedeker EC, Harpaz N, Pace NR. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci. 2007;104(34):13780–5.CrossRefGoogle Scholar
  24. 24.
    Gunzburg R. Adolescent idiopathic scoliosis: 70% agreement in expert opinion? : Springer; 2014.Google Scholar
  25. 25.
    Hida M, Aiba Y, Sawamura S, Suzuki N, Satoh T, Koga Y. Inhibition of the accumulation of uremic toxins in the blood and their precursors in the feces after oral administration of Lebenin®, a lactic acid bacteria preparation, to uremic patients undergoing hemodialysis. Nephron. 1996;74(2):349–55.CrossRefGoogle Scholar
  26. 26.
    Lam V, Su J, Koprowski S, Hsu A, Tweddell JS, Rafiee P, et al. Intestinal microbiota determine severity of myocardial infarction in rats. FASEB J. 2012;26(4):1727–35.CrossRefGoogle Scholar
  27. 27.
    Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444(7122):1022–3.CrossRefGoogle Scholar
  28. 28.
    Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55–60.CrossRefGoogle Scholar
  29. 29.
    Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, DuGar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011;472(7341):57–63.CrossRefGoogle Scholar
  30. 30.
    Lin CJ, Chen HH, Pan CF, Chuang CK, Wang TJ, Sun FJ, et al. p-Cresylsulfate and indoxyl sulfate level at different stages of chronic kidney disease. J Clin Lab Anal. 2011;25(3):191–7.CrossRefGoogle Scholar
  31. 31.
    Wu I-W, Hsu K-H, Lee C-C, Sun C-Y, Hsu H-J, Tsai C-J, et al. p-Cresyl sulphate and indoxyl sulphate predict progression of chronic kidney disease. Nephrol Dial Transplant. 2010;26(3):938–47.CrossRefGoogle Scholar
  32. 32.
    Guldris SC, Parra EG, Amenós AC. Gut microbiota in chronic kidney disease. Nefrología (English Edition). 2017;37(1):9–19.CrossRefGoogle Scholar
  33. 33.
    Mahmoodpoor F, Rahbar Saadat Y, Barzegari A, Ardalan M, Zununi VS. The impact of gut microbiota on kidney function and pathogenesis. Biomed Pharmacother = Biomedecine & pharmacotherapie. 2017;93:412–9.  https://doi.org/10.1016/j.biopha.2017.06.066.CrossRefGoogle Scholar
  34. 34.
    Pan W, Kang Y. Gut microbiota and chronic kidney disease: implications for novel mechanistic insights and therapeutic strategies. Int Urol Nephrol. 2018;50(2):289–99.CrossRefGoogle Scholar
  35. 35.
    Colares VS, Titan SM, Pereira Ada C, Malafronte P, Cardena MM, Santos S, et al. MYH9 and APOL1 gene polymorphisms and the risk of CKD in patients with lupus nephritis from an admixture population. PLoS One. 2014;9(3):e87716.  https://doi.org/10.1371/journal.pone.0087716.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Tzur S, Rosset S, Shemer R, Yudkovsky G, Selig S, Tarekegn A, et al. Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene. Hum Genet. 2010;128(3):345–50.  https://doi.org/10.1007/s00439-010-0861-0.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Genovese G, Tonna SJ, Knob AU, Appel GB, Katz A, Bernhardy AJ, et al. A risk allele for focal segmental glomerulosclerosis in African Americans is located within a region containing APOL1 and MYH9. Kidney Int. 2010;78(7):698–704.  https://doi.org/10.1038/ki.2010.251.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Bostrom MA, Freedman BI. The spectrum of MYH9-associated nephropathy. Clinical journal of the American Society of Nephrology : CJASN. 2010;5(6):1107–13.  https://doi.org/10.2215/CJN.08721209.CrossRefPubMedGoogle Scholar
  39. 39.
    Friedman DJ, Pollak MR. Apolipoprotein L1 and kidney disease in African Americans. Trends Endocrinol Metab. 2016;27(4):204–15.  https://doi.org/10.1016/j.tem.2016.02.002.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Parsa A, Kao WH, Xie D, Astor BC, Li M, Hsu CY, et al. APOL1 risk variants, race, and progression of chronic kidney disease. N Engl J Med. 2013;369(23):2183–96.  https://doi.org/10.1056/NEJMoa1310345.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Yadav AK, Kumar V, Sinha N, Jha V. APOL1 risk allele variants are absent in Indian patients with chronic kidney disease. Kidney Int. 2016;90(4):906–7.  https://doi.org/10.1016/j.kint.2016.07.026.CrossRefPubMedGoogle Scholar
  42. 42.
    Johnstone DB, Shegokar V, Nihalani D, Rathore YS, Mallik L, Ashish ZV, et al. APOL1 null alleles from a rural village in India do not correlate with glomerulosclerosis. PLoS One. 2012;7(12):e51546.  https://doi.org/10.1371/journal.pone.0051546.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • P. V. Parvati Sai Arun
    • 1
    • 2
    Email author
  • C. Obula Reddy
    • 1
  • Yusuf Akhter
    • 3
  1. 1.Department of BiotechnologyChaitanya Bharathi Institute of TechnologyHyderabadIndia
  2. 2.Institute of Bioinformatics and Computational BiologyVisakhapatnamIndia
  3. 3.Department of BiotechnologyBabasaheb Bhimrao Ambedkar UniversityLucknowIndia

Personalised recommendations