Abstract
Next-generation sequencing (NGS) has revolutionized the field of genomics and created new opportunities for basic research. We described the strategy for the NGS validation of the “dysglycaemia panel” composed by 44 genes related to glucose metabolism disorders (MODY, Wolfram syndrome) and familial renal glycosuria using Ion AmpliSeq technology combined with Ion-PGM. Anonymized DNA of 32 previously genotyped cases with 33 different variants were used to optimize the methodology. Standard protocol was used to generate the primer design, library, template preparation, and sequencing. Ion Reporter tool was used for data analysis. In all the runs, the mean coverage was over 200×. Twenty-nine out of thirty three variants (96.5%) were detected; four frameshift variants were missed. All point mutations were detected with high sensitivity. We identified three further variants of unknown significance in addition to pathogenic mutations previously identified by Sanger sequencing. The NGS panel allowed us to identify pathogenic variants in multiple genes in a short time. This could help to identify several defects in children and young adults that have to receive the genetic diagnosis necessary for optimal treatment. In order not to lose any pathogenic variants, Sanger sequencing is included in our analytical protocol to avoid missing frameshift variants.
1. Introduction
Dysglycaemia refers to an abnormality in blood glucose level, which can include hypoglycaemia (low blood glucose level) or hyperglycaemia (high blood glucose level). Monogenic diabetes is a rare form of inherited diabetes mellitus caused by heterozygous or homozygous defects in a single gene involved in β-cell development or function. It accounts for approximately 1% to 6% of pediatric diabetes patients [1]. In fact, until now, most cases of monogenic diabetes remain undiagnosed [2]. The most common type of monogenic diabetes is MODY (maturity-onset diabetes of the young, MIM # 606391) and, to date, 14 subtypes are known [3] representing 2–5% of diabetes cases in Europe [4,5,6,7]. Permanent neonatal diabetes mellitus (PNDM, MIM #606176) is another form of monogenic diabetes characterized by persistent hyperglycemia within the first 12 months of life in general, requiring continuous treatment. Affected patients often respond well to sulfonylureas, and insulin may not be necessary. Wolfram syndrome (WS, MIM #222300), a rare autosomal recessive neurodegenerative disorder characterized by diabetes insipidus, diabetes mellitus, optic atrophy, and deafness, is caused by bi-allelic defects in WFS1 gene. Patients with familial renal glycosuria (FRG, MIM #233100) have decreased renal tubular reabsorption of glucose from urine in the absence of hyperglycemia and any other signs of tubular dysfunction. It is caused by SLC5a2 mono or bi-allelic mutations. Congenital hyperinsulinism (CHI, MIM #256450) is a rare genetic disorder caused by genetic mutations in several genes that lead to an excess of insulin secretion in pancreatic β-cells. Pathogenic variations in KATP channels are the most common cause.
Genetic testing is strongly recommended for a rapid and accurate diagnosis of monogenic diabetes or CHI in order to allow the clinician to make a correct diagnosis.
In the past 10 years, the next-generation sequencing (NGS) has revolutionized the field of genomics and created new opportunities for basic research [8,9]. NGS enables the development of faster, more comprehensive and cost-effective genetic methods compared to conventional Sanger sequencing.
In this manuscript, we aim to describe the strategy for the validation of a home-made NGS “dysglycaemia panel” consisting of 44 candidate genes related to glucose metabolism disorders (MODY, WS) and FRG, using Ion AmpliSeq technology combined with Ion-PGM. We also reported the results of the first genetic analyses.
2. Materials and Methods
2.1. Cases Used for Validation
All the procedures adopted for the validation of the home-made NGS “dysglycaemia panel” follow the guidelines reported in ACMG Standards for clinical next-generation sequencing [10].
Sequencing
Thirty-two anonymized DNA samples of patients, previously genotyped by Sanger sequencing, were used to optimize the methodology and perform the panel validation. Different types of mutations such as missense, nonsense, frameshift (Figure 1A) localized in genes responsible for monogenic diabetes, WS, and FRG (Figure 1B) were chosen.
Figure 1.
Distribution of types of mutations (A) and genes (B) used to optimize the NGS panel.
2.2. NGS Ampliseq Protocol
Genomic DNA was extracted from EDTA whole blood using QIAamp DNA Blood Midi kit (Qiagen GmbH, Hilden, Germany) and purified with Amicon Ultra 0.5 mL (Merk Millipore LTD, IRL) according to standard procedures. Eluted DNA was quantified with Nanodrop Spectrometer (Thermo Fisher Scientific, Waltham, MA, USA).
Primers for 44 genes causative of dysglycaemia and its complications were designed using the Thermo Fisher Scientific Ion AmpliSeq Designer platform (version 5.6; www.ampliseq.com (accessed on 30 March 2023)) according to hg19. The list of the genes included in the NGS panel is reported in Table 1.
Table 1.
“List of genes included in on demand panel” composed by 44 genes causative of dysglycaemia and its complications. AD—autosomal dominant, AR—autosomal recessive, XLR—X-linked recessive, ND—not determined.
The primer pairs allowed the amplification of the exons and their flanking regions of all 44 genes. The resulting panel size was 172.857 kb containing 900 amplicons with a coverage of 99% of the targeted regions. All the uncovered parts of the design were sequenced by Sanger sequencing.
A total of 15 ng of DNA from each sample was combined with the Ampliseq reagents and primer pool for the dysglycaemia according to standard protocol. After this step, the amplicon products were partially digested, and IonExpress Adapters and Barcode sequences were ligated to the library fragments. In the following step, the barcoding libraries were cleaned up using a magnetic bead method (Ion Torrent, Thermo Fisher Scientific, Waltham, MA, USA) and then quantified following the Qubit 2.0 fluorimeter (Thermo Fisher Scientific, Waltham, MA, USA) instructions using HS dsDNA kit. The quantified libraries were pooled and diluted once to 100 pM.
The library was amplified by emulsion PCR on Ion sphere particles (ISPs) using the Ion PGM Hi-Q OT2 Kit according to standard protocol (Life Technologies, Carlsbad, CA, USA). Ion 316 chips were used to sequence eight samples simultaneously. Sequencing was performed on an Ion PGM System (Ion Torrent, Thermo Fisher Scientific, Waltham, MA, USA) using the Ion PGM Hi-Q Sequencing kit according to the manufacturer’s instructions. Sequencing data were analyzed with Coverage Analysis and Variant Caller plugins available within the Ion Torrent Suite software TS 5.18 and contextually with Ion Reporter. All the pathogenic or likely pathogenic variants detected by Ion PGM were confirmed using specific couples of primers by direct sequencing of the PCR products.
3. Results
In all the runs, the mean coverage was over 200×, and the mean length of the amplicons was about 200 bp. All data of the variants selected for the evaluation of the “dysglycaemia panel” performance are reported in Table 2, and the results of performance metrics are shown in Table 3. Twenty-nine mutations out of thirty-three were detected by NGS sequencing, while four were missed. Three were frameshift variants ≥ 10bp (c.1279_1358delinsTTACA in GCK exon 10, c.1342_1374del in HNF1a exon 7, c.1261_1280dup19 in SLC5a2 exon 10). One was the duplication of c.872dupC in HNF1a exon 4. Three further variants, classified as variants of uncertain significance (VUS), were detected; all of them were found in the DNA of three unrelated cases in addition to pathogenic variants (Table 4).
Table 2.
Validation data of the “dysglycaemia panel”. ACMG—American College of Medical Genetics and Genomics, Hom—homozygous, P—pathogenic, LP—likely pathogenic, VUS—variants of uncertain significance, B—benign, Nd—not detected. VUS were described in the literature and liked to clinical phenotype.
Table 3.
Analytical validation of the “dysglycaemia panel”.
Table 4.
Analysis of the three variants in addition to the variants previously identified by Sanger Sequencing. Het—heterozygous, ACMG—American College of Medical Genetics and Genomics, Mut Taster—MutationTaster, HGMD—Human Gene Mutation Database, VUS—variants of uncertain significance, LP—likely pathogenic, Poss D—possibly damaging, Prob D—probably damaging, D—damaging, Ref—references.
4. Discussion
In this study, we report the successful validation of a custom-designed targeted Ampliseq panel for the detection of variants causative of dysglycaemia and its complications. Recently, in the literature, several reports on the application of NGS approach for genetic diagnosis of monogenic diabetes have been published [17,18,19]. All of them are designed to allow the detection of genetic defects causative of monogenic diabetes mellitus (i.e., MODY and neonatal diabetes mellitus) and rare diabetes-associated syndromes (i.e., Wolfram syndrome, Alström syndrome, Wolcott–Rallison syndrome, and thiamine-responsive megaloblastic anemia (TRMA)/Roger’s syndrome) [20]. Based on this information, 44 different genes that are commonly mutated in monogenic diabetes mellitus, congenital hyperinsulinism, Wolfram Syndrome, and familial renal glycosuria were been included in our panel design. We selected anonymized subjects previously genotyped in which thirty-three different variants were present: twenty-nine point mutations or frameshift variants causative of deletion/insertion <10 bp and four frameshift variants caused by deletion/insertion ≥10 bp. The molecular test confirms a good ability to detect point and deletion/insertion mutations of less than 10 bp. In fact, 96.5% of the variants were confirmed. Regarding frameshift variants greater than 10 bp, only one out of four (25%) was detected by the NGS test. Sequencing errors remain a major challenge in the single nucleotide analysis using an NGS platform [21,22].
Ion torrent sequencing system performed by PGM measures the hydrogen ions released during the incorporation of dNTP in the amplification of the DNA target template. The protons release causes a decrease of the pH level in the solution present inside the chip. Ph variation is directly proportional to the number of bases incorporated in the template. When target region is rich of homopolymer repeats, the multiple incorporation of dNTP may lead to a high number of hydrogens released, and a high electronic signal was detected by the sensor. Consequently, variants occurring in this genomic region may be missed [23]
In our study, all the four missed frameshift mutations are not located in the genomic region rich of homopolymers on repeat regions. Therefore, we may suppose that the failure of detection was related to the primer design used for the library amplification, even if the theoretical coverage of HNF1a, GCK, and SLC5a2 genes was predicted as 100% by the Ampliseq primer design tools. As a consequence of this fact, in order to detect these variants, we decided to include in our protocol the Sanger sequencing of GCK exon 10, HNF1a exon 4 and 7, and SLC5a2 exon 11 using specific pairs of primers (available on request).
MODY is defined as an autosomal inherited form of diabetes mellitus caused by mutations in a single gene. In 2013, Lopez et al. described the co-inheritance of HNF1a and GCK mutation in one MODY patient [24]. In the last few years, with the advent of the application of NGS sequencing for genetic diagnosis of monogenic diabetes, further cases of MODY caused by defects in different genes involved in insulin release in response to blood glucose levels have been described [25]. In our validation experiments, three variants that we classified to the best of our knowledge as VUS were detected in two subjects with heterozygous GCK defects and in one with Wolfram syndrome diagnosis (Table 3). Due to the anonymization of the sample used for the panel validation, it is not an aim of this paper to discuss the role of the identified VUS in the clinical phenotype of the three patients. Herein, we only suppose that the difficulty to establish a genotype/phenotype correlation in patients with monogenic diabetes, or to understand the reason that patients with the same defect have frequently different clinical manifestations, may be related to the presence of unidentified variants in other genes that have not been sequenced. This happens principally when genetic diagnosis is performed by Sanger sequencing.
5. Conclusions
In conclusion, our panel based on the Ampliseq approach can be useful for identifying pathogenic variants in multiple genes in a short time. This could help to detect several defects in children and young adults who need to receive a genetic diagnosis necessary for optimal treatment. Sanger sequencing will be included in our analytical protocol to avoid missing frameshift variants due to dysglycaemia.
Author Contributions
Conceptualization, A.S. and C.A.; methodology, A.S., C.A. and F.C.; validation and formal analysis, A.S., C.A. and R.B.; data curation, A.S. and C.A.; writing—original draft preparation, A.S., C.A. and B.T.; writing—review and editing, A.S., C.A., B.T., M.B., N.M., G.d. and M.M.; supervision, N.M., G.d. and M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the “Cinque per mille” and “Ricerca corrente” (Italian Ministry of Health).
Institutional Review Board Statement
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of IRCCS Istituto Giannina Gaslini.
Informed Consent Statement
Informed consent was obtained from all subjects involved in this study.
Data Availability Statement
Not applicable.
Acknowledgments
We are grateful to the Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genova—Department of Excellence—for their support in several steps leading to the approval and publication of this study. This study was also developed within the framework of the DINOGMI, Department of Excellence of MIUR 2018–2022 (legge 232 del 2016).
Conflicts of Interest
The authors declare no conflict of interest.
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