Investigations of Kidney Dysfunction-Related Gene Variants in Sickle Cell Disease Patients in Cameroon (Sub-Saharan Africa)

Abstract

BackgroundRenal dysfunctions are associated with increased morbidity and mortality in sickle cell disease (SCD). Early detection and subsequent management of SCD patients at risk for renal failure and dysfunctions are essential, however, predictors that can identify patients at risk of developing renal dysfunction are not fully understood.MethodsIn this study, we have investigated the association of 31 known kidney dysfunctions-related variants detected in African Americans from multi-ethnic genome wide studies (GWAS) meta-analysis, to kidney-dysfunctions in a group of 413 Cameroonian patients with SCD. Systems level bioinformatics analyses were performed, employing protein-protein interaction networks to further interrogate the putative associations.ResultsUp to 61% of these patients had micro-albuminuria, 2.4% proteinuria, 71% glomerular hyperfiltration, and 5.9% had renal failure. Six variants are significantly associated with the two quantifiable phenotypes of kidney dysfunction (eGFR and crude-albuminuria): A1CF-rs10994860 (P = 0.02020), SYPL2-rs12136063 (P = 0.04208), and APOL1 (G1)-rs73885319 (P = 0.04610) are associated with eGFR; and WNT7A-rs6795744 (P = 0.03730), TMEM60-rs6465825 (P = 0.02340), and APOL1 (G2)-rs71785313 (P = 0.03803) observed to be protective against micro-albuminuria. We identified a protein-protein interaction sub-network containing three of these gene variants: APOL1, SYPL2, and WNT7A, connected to the Nuclear factor NF-kappa-B p105 subunit (NFKB1), revealed to be essential and might indirectly influence extreme phenotypes. Interestingly, clinical variables, including body mass index (BMI), systolic blood pressure, vaso-occlusive crisis (VOC), and haemoglobin (Hb), explain better the kidney phenotypic variations in this SCD population.ConclusionThis study highlights a strong contribution of haematological indices (Hb level), anthropometric variables (BMI, blood pressure), and clinical events (i.e., vaso-occlusive crisis) to kidney dysfunctions in SCD, rather than known genetic factors. Only 6/31 characterised gene-variants are associated with kidney dysfunction phenotypes in SCD samples from Cameroon. The data reveal and emphasise the urgent need to extend GWAS studies in populations of African ancestries living in Africa, and particularly for kidney dysfunctions in SCD.

Systematic Review of Genetic Modifiers Associated with the Development and/or Progression of Nephropathy in Patients with Sickle Cell Disease

“…Similarly, one APOL1 G1 allele (rs73885319) was associated with proteinuria in adults [23]. A protective effect of one G2 allele against albuminuria in children was also demonstrated by Ngo-Bitoungui et al [36].…”

“…-reference, S-short GT-tandem repeat, Sbeta0-compound heterozygous hemoglobin S/beta 0 -thalassemia, SCA-sickle cell anemia, 1 defined as dipstick 1+ or more, 2 genetic risk profile defined as SCA patients with APOL1 HRG, without alpha-thalassemia and with wild-type BCL11 rs1427407 allele, 4 proteinuria measured by dipstick (≥ 1+ or more) and quantitatively (≥30 mg/dl), 5 only in combination with α −3.7 deletion, 3 Ten studies investigated the role of APOL1 in the development of albuminuria [9,23,26,29,30,33,36,37,39,43]. Of these, five studies included only children (HbSS in n = 3 [36,39,43], HbSS and HbS/beta 0 -thalassemia in n = 2 [33,37]); four other studies included only adults (all genotypes in n = 3 [23,26,29], HbSS and HbS/beta 0 -thalassemia in n = 1 [30]). A single study included homozygous SCA patients of all ages [9].…”

“…Secondly, two variants in HMOX1 were investigated: three studies evaluated the effect of the length of HMOX1 GT-dinucleotide repeats [9,36,39] and six studies evaluated an SNP (rs743811) [9,26,29,34,36,37]. HMOX1 GT-dinucleotide-long repeats were not associated with albuminuria in SCA patients.…”

Genetic Variation and Sickle Cell Disease Severity

“…We identified 571 manuscripts published before May 16, 2023, reporting genotype-phenotype associations across 29 670 unique individuals (50% ≤ 18 years of age) from 43 countries (eMethods, eFigures 1 and 2, and eTable 5 in Supplement 1 ; eTable 1 in Supplement 2 ). 8 , 11 , 12 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ,…”

“…A total of 571 publications passed this screening (eFigure 1 in Supplement 1 , eTable 1 in Supplement 2 ). 8 , 11 , 12 , 13 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ...…”