Association of KLOTHO polymorphisms with clinical complications of sickle cell anemia

Batista, Jéssica V G F; Pereira-Martins, Diego A; Falcão, Daniela; Domingos, Igor F.; Arcanjo, Gabriela da Silva; Hatzlhofer, Betânia Lucena Domingues; Weinhäuser, Isabel; Batista, Thais Helena Chaves; Cardoso, Pablo Ramon Gualberto; Anjos, Ana Claudia; Hazin, Manuela F; Pitta, M. G. R.; Costa, Fernando Ferreira; Araújo, Aderson S; Lucena-Araújo, Antonio R.; Bezerra, Marcos André Cavalcanti

doi:10.1007/s00277-021-04532-w

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…This comprehensive systematic review and meta-analysis consolidates current knowledge of SCD Bacteremia, occurrence 365 Bacterial infection, occurrence 148 Urinary infection, occurrence 300 Viral infection, occurrence 148 Leg ulcers, occurrence 118,148 Hepatomegaly, ≥4 cm vs >4 cm 207 Priapism, occurrence 21,118,148 Splenectomy, occurrence 438 Osteonecrosis, occurrence 148 Hematocrit 118 MCH 118 MCV 118 RBC count 118 UACR 170 Albuminuria, occurrence 170 Albuminuria, time to event 20,320,514 eGFR, adult cohort 170 eGFR, pediatric cohort, Schwartz equation 514 eGFR, <60 vs ≥60 170 Glomerular hyperfiltration, time to event 514 Glomerular hyperfiltration, mGFR>110 vs ≤110 326 CKD progression, time to event 170 Renal length 429 Border classification, occurrence 438 Patches, occurrence 438 Posterior border, occurrence 438 Phenotype ACS, occurrence 148 Pneumonia, occurrence 300 Respiratory infections, occurrence 300 VOC, ≥3 episodes vs none 83 VOC, occurrence 148,207,300 VOC, No. events/y 17 Blood transfusion, occurrence 148,207 Hemoglobin 118,436 Hemoglobin, low vs high 120 Hemolytic component, continuous score…”

Section: Discussionmentioning

confidence: 74%

“…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 ,…”

Section: Resultsmentioning

confidence: 99%

“…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 ...…”

Section: Methodsunclassified

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Genetic Variation and Sickle Cell Disease Severity

Kirkham,

Estepp,

Weiss

et al. 2023

JAMA Netw Open

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ImportanceSickle cell disease (SCD) is a monogenic disorder, yet clinical outcomes are influenced by additional genetic factors. Despite decades of research, the genetics of SCD remain poorly understood.ObjectiveTo assess all reported genetic modifiers of SCD, evaluate the design of associated studies, and provide guidelines for future analyses according to modern genetic study recommendations.Data SourcesPubMed, Web of Science, and Scopus were searched through May 16, 2023, identifying 5290 publications.Study SelectionAt least 2 reviewers identified 571 original, peer-reviewed English-language publications reporting genetic modifiers of human SCD phenotypes, wherein the outcome was not treatment response, and the comparison was not between SCD subtypes or including healthy controls.Data Extraction and SynthesisData relevant to all genetic modifiers of SCD were extracted, evaluated, and presented following STREGA and PRISMA guidelines. Weighted z score meta-analyses and pathway analyses were conducted.Main Outcomes and MeasuresOutcomes were aggregated into 25 categories, grouped as acute complications, chronic conditions, hematologic parameters or biomarkers, and general or mixed measures of SCD severity.ResultsThe 571 included studies reported on 29 670 unique individuals (50% ≤ 18 years of age) from 43 countries. Of the 17 757 extracted results (4890 significant) in 1552 genes, 3675 results met the study criteria for meta-analysis: reported phenotype and genotype, association size and direction, variability measure, sample size, and statistical test. Only 173 results for 62 associations could be cross-study combined. The remaining associations could not be aggregated because they were only reported once or methods (eg, study design, reporting practice) and genotype or phenotype definitions were insufficiently harmonized. Gene variants regulating fetal hemoglobin and α-thalassemia (important markers for SCD severity) were frequently identified: 19 single-nucleotide variants in BCL11A, HBS1L-MYB, and HBG2 were significantly associated with fetal hemoglobin (absolute value of Z = 4.00 to 20.66; P = 8.63 × 10−95 to 6.19 × 10−5), and α-thalassemia deletions were significantly associated with increased hemoglobin level and reduced risk of albuminuria, abnormal transcranial Doppler velocity, and stroke (absolute value of Z = 3.43 to 5.16; P = 2.42 × 10−7 to 6.00 × 10−4). However, other associations remain unconfirmed. Pathway analyses of significant genes highlighted the importance of cellular adhesion, inflammation, oxidative and toxic stress, and blood vessel regulation in SCD (23 of the top 25 Gene Ontology pathways involve these processes) and suggested future research areas.Conclusions and RelevanceThe findings of this comprehensive systematic review and meta-analysis of all published genetic modifiers of SCD indicated that implementation of standardized phenotypes, statistical methods, and reporting practices should accelerate discovery and validation of genetic modifiers and development of clinically actionable genetic profiles.

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Section: Discussionmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 99%

Section: Methodsunclassified

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Genetic Variation and Sickle Cell Disease Severity

Kirkham,

Estepp,

Weiss

et al. 2023

JAMA Netw Open

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“…ADORA2A [716], DPEP1 [717], KMO (kynurenine 3-monooxygenase) [718], E2F1 [719], VASH2 [720], ARG2 [721], MSRA (methionine sulfoxidereductase A) [722], NOX4 [723], AQP2 [724], EGF (epidermal growth factor) [725], ANGPT1 [726], KL (klotho) [727], ACE2 [728], NR4A1 [729], SLC47A1 [358], LYVE1 [730], AQP3 [731], DPP4 [732], STC1 [733], TREH (trehalase) [734], CCR1 [735], VCAM1 [736], PLAT (plasminogen activator, tissue type) [737] and ALB (albumin) [738] might serve as potential biomarkers for renal ischemia. GLRX5 [739], AMH (anti-Mullerian hormone) [740], HOXB4 [741], COMP (cartilage oligomeric matrix protein) [742], FLI1 [743], PCK1 [744], CUBN (cubilin) [745], CYP2C8 [746], BPGM (bisphosphoglyceratemutase) [747], SLC19A2 [578], HRG (histidine rich glycoprotein) [748], KL (klotho) [749], REN (renin) [414], ABCB1 [750], F8 [751], VCAM1 [752], ACVR1 [753] and PLAT (plasminogen activator, tissue type) [754] might be regarded as a valuable biomarker for diagnosis, treatment and prognosis of anemia. Altered expression of BRSK1 [755], SCNN1G [756], CADM1 [757], PODXL (podocalyxin like) [758], NPHS1 [759], CNTN1 [760], NTNG1 [761], PLAT (plasminogen activator, tissue type) [259] and P3H2 [762] are involved in the development and progression of membranous nephropathy.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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“…Multiple studies have investigated the association of variants in TNF-alpha (14,(16)(17)(18), MTHFR (19)(20)(21)(22), and VCAM-1 (17,18,23) with stroke in SCD patients. Similarly, studies have also investigated association of genetic variants and have shown association of ADCY9 (24), TEK (18,24), GOLGB1 (15,18), ENPP1 (15,17,18), PON1 (12,17), ANXA2 (12,16), KL (25), Alpha 3.7 thal del (21), βS Haplotype (26), SOD2 (27), and BCL11 (28) with stroke. This list not only includes single nucleotide polymorphism genotyping studies but also includes genome-wide association studies (29,30), whole exome studies (15,31), and whole genome studies (32).…”

Section: Introductionmentioning

confidence: 99%

Genetic variants associated with the risk of stroke in sickle cell disease: a systematic review and meta-analysis

Kumari

Chauhan

Chaudhuri

et al. 2023

Preprint

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Background: Sickle cell disease (SCD) is the commonest cause of stroke in children. As it is a rare disease, studies investigating the association with complications like stroke in SCD have small sample sizes. Here, we performed a systematic review and meta-analysis of the studies exploring an association of genetic variants with stroke to get a better indication of their association with stroke. Methods: PubMed and Google Scholar were searched to identify studies that had performed an association analysis of genetic variants for the risk of stroke in SCD patients. After screening of eligible studies, summary statistics of association analysis with stroke and other general information were extracted. Meta-analysis was performed using the fixed effect method on the tool METAL and forest plots were plotted using the R program. The random effect model was performed as a sensitivity analysis for loci where significant heterogeneity was observed. Results: 407 studies were identified using the search term and after screening 39 studies that cumulatively analysed 11,780 SCD patients were included. These 39 studies included a total of 2,401 SCD patients with stroke, predominantly included individuals of African ancestry (N=16). Three of these studies performed whole exome sequencing while 35 performed single nucleotide-based genotyping. Though the studies reported association with 109 loci, meta-analyses could be performed only for 12 loci that had data from two or more studies. After meta-analysis we observed that four loci were significantly associated with risk for stroke: -α3.7kb Alpha-thalassemia deletion (P= 0.00000027), rs489347-TEK (P= 0.00081), rs2238432-ADCY9 (P= 0.00085) and rs11853426-ANXA2 (P= 0.0034). Conclusion: Ethnic representation of regions with a high prevalence of SCD like the Mediterranean basin and India needs to be improved for genetic studies on associated complications like stroke. Larger genome-wide collaborative studies on SCD and associated complications including stroke needs to be performed.

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Association of KLOTHO polymorphisms with clinical complications of sickle cell anemia

Cited by 5 publications

References 31 publications

Genetic Variation and Sickle Cell Disease Severity

Genetic Variation and Sickle Cell Disease Severity

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Genetic variants associated with the risk of stroke in sickle cell disease: a systematic review and meta-analysis

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