Development of next generation sequencing panel for UMOD and association with kidney disease

Bailie, Caitlin; Kilner, Jill; Maxwell, Alexander P.; McKnight, Amy Jayne

doi:10.1371/journal.pone.0178321

Cited by 5 publications

(7 citation statements)

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“…Ninety of those variants were SNPs, 60 of them with minor allele frequency greater than 5%. Linkage disequilibrium allowed 20 SNPs to capture 100% of the alleles with a mean R 2 of 0.97, providing a set of independent SNPs suitable for association analysis in larger cohorts (Bailie et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Genetic Susceptibility to Chronic Kidney Disease – Some More Pieces for the Heritability Puzzle

et al. 2019

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Chronic kidney disease (CKD) is a major global health problem with an increasing prevalence partly driven by aging population structure. Both genomic and environmental factors contribute to this complex heterogeneous disease. CKD heritability is estimated to be high (30–75%). Genome-wide association studies (GWAS) and GWAS meta-analyses have identified several genetic loci associated with CKD, including variants in UMOD, SHROOM3 , solute carriers, and E3 ubiquitin ligases. However, these genetic markers do not account for all the susceptibility to CKD, and the causal pathways remain incompletely understood; other factors must be contributing to the missing heritability. Less investigated biological factors such as telomere length; mitochondrial proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded genes; structural variants, such as copy number variants (CNVs), insertions, deletions, inversions and translocations are poorly covered and may explain some of the missing heritability. The sex chromosomes, often excluded from GWAS studies, may also help explain gender imbalances in CKD. In this review, we outline recent findings on molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes) that typically have received less attention than gene polymorphisms. Shorter telomere length has been associated with renal dysfunction and CKD progression, however, most publications report small numbers of subjects with conflicting findings. CNVs have been linked to congenital anomalies of the kidney and urinary tract, posterior urethral valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA biomarkers for CKD comes primarily from case reports, therefore the data are scarce and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene, mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found associations between X-chromosome and renal function (rs12845465 and rs5987107). No loci in the Y-chromosome have reached genome-wide significance. In conclusion, despite the efforts to find the genetic basis of CKD, it remains challenging to explain all of the heritability with currently available methods and datasets. Although additional biomarkers have been investigated in less common suspects such as telomeres, CNVs, mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more comprehensive approaches, particularly through the integration of multiple –“omics” data, are needed.

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Section: Introductionmentioning

confidence: 99%

Genetic Susceptibility to Chronic Kidney Disease – Some More Pieces for the Heritability Puzzle

et al. 2019

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“…Twenty patients with “rapid disease progression” and 20 patients with “stable kidney function,” determined using a mixed effects linear regression model using two measures of eGFR, had their methylation status assessed using the 450 K Array [ 98 ]. Although no significant association with CKD progression could be found after correction for multiple testing, several candidate genes (including UMODL1 ) that had previously been related with CKD in the literature [ 96 , 98 , 100 ] showed a trend towards association with rapid CKD progression.…”

Section: Epigenetic Biomarkersmentioning

confidence: 81%

“…So while this may suggest that DNA methylation plays a role in regulating their expression, further analysis is required in a larger number of patients, as only two Stage 5 CKD patients and two controls were used in expression studies [ 97 ]. Further study in this group of patients was focused on the differential methylation of the UMOD and related genes UMODL1 and UMODL1 - AS1 genes [ 100 ], previously associated with CKD in GWAS [ 23 , 25 ]. Indeed, 11 CpGs across the three genes were found to be significantly differentially methylated in patients with CKD ( p < 10 −8 ) [ 100 ].…”

Section: Epigenetic Biomarkersmentioning

confidence: 99%

“…Further study in this group of patients was focused on the differential methylation of the UMOD and related genes UMODL1 and UMODL1 - AS1 genes [ 100 ], previously associated with CKD in GWAS [ 23 , 25 ]. Indeed, 11 CpGs across the three genes were found to be significantly differentially methylated in patients with CKD ( p < 10 −8 ) [ 100 ].…”

Section: Epigenetic Biomarkersmentioning

confidence: 99%

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Genomic approaches in the search for molecular biomarkers in chronic kidney disease

et al. 2018

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BackgroundChronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD.Main bodyConventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management.ConclusionCKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases.

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“…Targeted sequencing platforms that leverage amplification or hybridization capture of specific nucleotide sequences have already demonstrated clinical utility for the detection and analysis of numerous human genetic conditions, including cancer, cardiovascular disease, immune deficiency, and a range of inherited diseases ( 8 – 10 ). Furthermore, efforts to identify sequencing targets and apply them for the detection of microbial pathogens, such as respiratory syncytial virus ( 11 ), Ebola virus ( 12 ), and drug-resistant Mycobacterium tuberculosis ( 13 ), have been routinely explored.…”

Section: Introductionmentioning

confidence: 99%

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

Kok,

Mulakken,

Thissen

et al. 2023

Microbiol Spectr

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Current diagnostics and clinical management strategies for combat wounds are based on decisions made by expert clinicians. However, even in the hands of experienced surgeons, wounds from combat injuries can exhibit failed healing and complications related to limitations in the rapid and comprehensive generation of diagnostic information. Previous studies have demonstrated the possible use of genomic sequencing approaches to detect microbial signatures involved in combat casualty care. While effective, whole metagenome sequencing is limited by the depth required to confidently detect all relevant signatures. To address this, we developed a targeted capture sequencing panel to detect microbial signatures relevant to wound healing. These targets include known microbial nosocomial pathogens, wound colonizers, and genes involved in virulence and antimicrobial resistance. A bioinformatics pipeline was built to identify genomic regions of interest and over 8,000 oligonucleotide probes were designed for capture. The panel was synthesized and validated using control reference genomes in human background and on wound-effluent samples from a cohort of combat-injured U.S. service members. Our panel was sensitive against wound-colonizing species, Acinetobacter baumannii and Pseudomonas aeruginosa , and was specific in detecting corresponding virulence and antimicrobial-resistance genes as well as other pathogenic species present in microflora mixtures. Random forest feature permutation confirmed the prevalence of Acinetobacter and Pseudomonas in critically colonized wounds and wounds that failed to heal, respectively. Our results demonstrate the capability of targeted sequencing tools and analysis platforms to profile and deliver information on pathogenic factors influencing wound progression, thereby guiding therapeutic intervention. IMPORTANCE Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

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Development of next generation sequencing panel for UMOD and association with kidney disease

Cited by 5 publications

References 49 publications

Genetic Susceptibility to Chronic Kidney Disease – Some More Pieces for the Heritability Puzzle

Genetic Susceptibility to Chronic Kidney Disease – Some More Pieces for the Heritability Puzzle

Genomic approaches in the search for molecular biomarkers in chronic kidney disease

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

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