Clinical application of ACMG-AMP guidelines in HNF1A and GCK variants in a cohort of MODY families

Genetic and clinical features of neonatal and early onset diabetes mellitus in a tertiary center cohort in Brazil

Neonatal diabetes mellitus (NDM) is defined as the occurrence of severe hyperglycemia in infants under 6 months old and may be permanent (PNDM) or transient (TNDM). When diabetes is diagnosed at 6-12 months of age (early onset diabetes [EOD]), the etiology may be monogenic; however, most cases consist of type 1 diabetes mellitus (T1DM). Molecular diagnosis was determined in a cohort of 35 unrelated Brazilian patients with NDM or EOD based on targeted next-generation sequencing panel and/or chromosome 6q24 abnormalities. The impact of genetic testing on treatment and follow-up was evaluated. Overall, 24 patients had NDM: with 18 (75.0%) having PNDM, 5 TNDM (20.8%) and 1 case in which this information was unknown. Eleven patients had EOD. Genetic testing was positive in 20/24 patients with NDM (83.3%) and in 18.2% of cases of EOD. The commonest causes were ATP-sensitive potassium (KATP) channel genes, and GCK and IPEX mutations (37.1%, 11.4% and 5.7%, respectively). Patients with PNDM due to KCNJ11 and ABCC8 mutations transitioned successfully to sulfonylureas in almost 60% of cases, reinforcing the benefit of performing genetic testing in NDM as early as possible. This report refers to the largest series of cases of NDM (TNDM and PNDM) and EOD in Brazil in which patients were submitted to molecular investigation and in which the clinical impact of genetic diagnosis was also evaluated.

The performance of the MODY calculator in a non-Caucasian, mixed-race population diagnosed with diabetes mellitus before 35 years of age

BACKGROUND

A maturity-onset diabetes of the young (MODY) calculator has been described and validated for use in European Caucasians. This study evaluated its performance in Brazilians diagnosed with diabetes mellitus (DM) before 35 years of age.

METHODS

The electronic records of 391 individuals were reviewed in 2020 at the diabetes clinic of a quaternary hospital in São Paulo were analyzed: 231 with type 1 DM (T1DM), 46 with type 2 (T2DM) and 114 with MODY. The MODY calculator was applied to the three groups. A receiver operating characteristic curve was calculated to obtain cut-off points for this population.

RESULTS

The principal differences between the MODY and the T1DM and T2DM groups were body mass index, a positive family history of diabetes and mean HbA1c level. Age at diagnosis in the MODY group was only significantly different compared to the T2DM group. Specificity and sensitivity were good for the cut-off points of 40%, 50% and 60%, with the accuracy of the model for any of these cut-off points being > 95%.

CONCLUSION

The capacity of the calculator to identify Brazilian patients with MODY was good. Values ≥ 60% proved useful for selecting candidates for MODY genetic testing, with good sensitivity and specificity.

Prevalence of maturity-onset diabetes of the young in phenotypic type 2 diabetes in young adults: a nationwide, multi-center, cross-sectional survey in China

BACKGROUND

Maturity-onset diabetes of the young (MODY) is the most common monogenic diabetes. The aim of this study was to assess the prevalence of MODY in phenotypic type 2 diabetes (T2DM) among Chinese young adults.

METHODS

From April 2015 to October 2017, this cross-sectional study involved 2429 consecutive patients from 46 hospitals in China, newly diagnosed between 15 years and 45 years, with T2DM phenotype and negative for standardized glutamic acid decarboxylase antibody at the core laboratory. Sequencing using a custom monogenic diabetes gene panel was performed, and variants of 14 MODY genes were interpreted as per current guidelines.

RESULTS

The survey determined 18 patients having genetic variants causing MODY (6 HNF1A , 5 GCK , 3 HNF4A , 2 INS , 1 PDX1 , and 1 PAX4 ). The prevalence of MODY was 0.74% (95% confidence interval [CI]: 0.40-1.08%). The clinical characteristics of MODY patients were not specific, 72.2% (13/18) of them were diagnosed after 35 years, 47.1% (8/17) had metabolic syndrome, and only 38.9% (7/18) had a family history of diabetes. No significant difference in manifestations except for hemoglobin A1c levels was found between MODY and non-MODY patients.

CONCLUSION

The prevalence of MODY in young adults with phenotypic T2DM was 0.74%, among which HNF1A -, GCK -, and HNF4A -MODY were the most common subtypes. Clinical features played a limited role in the recognition of MODY.

Mutations in GCK May Lead to MODY2 by Reducing Glycogen Synthesis

Dysfunction of glucokinase (GCK) caused by mutations in the GCK gene is the main cause of maturity-onset diabetes of the young type-2 (MODY2, also known as GCK-MODY), which is usually present in adolescence or young adulthood. MODY2 is characterized by mild, stable fasting hyperglycemia that presents at birth, usually 5.4-8.3 mmol L^-1 , and rarely develops complications from diabetes. The treatment of MODY2 prefers a manageable diet rather than the use of insulin. Previous studies have identified GCK mutations only by online software prediction or enzyme kinetic analysis and thermolability assays which are complicated to be conducted. In this study, six mutations in the GCK gene, including four novel mutations and two mutations that are previously reported, are identified. All the six locations are highly conserved according to the sequencing alignment. Moreover, missense mutations are strongly predicted to be pathogenic using online programs. Functional studies show that mutations in GCK mutation do not affect insulin secretion but affect glycogen synthesis. These findings demonstrate that GCK mutations decrease glycogen synthesis, which leads to hyperglycemia in MODY2. Meanwhile, this study provides a new perspective and methods for identifying pathogenic mutations in GCK.

Next Generation Sequencing Analysis of MODY-X Patients: A Case Report Series

BACKGROUND

Classic criteria for a maturity-onset diabetes of the young (MODY) diagnosis are often unable to identify all subjects, and traditional Sanger sequencing, using a candidate gene approach, leads to a high prevalence of missed genetic diagnosis, classified as MODY-X. Next generation sequencing (NGS) panels provide a highly sensitive method even for rare forms.

METHODS

We investigated 28 pediatric subjects suspected for MODY-X, utilizing a 15-gene NGS panel for monogenic diabetes (MD).

RESULTS

NGS detected variants of uncertain significance (VUS), likely pathogenic or pathogenic for rarer subtypes of MODY, in six patients. We found variants in the wolframin gene (WFS1), traditionally not considered in MD genetic screening panels, in three patients; KCNJ11 gene mutation, typically responsible for neonatal diabetes and rarely causing isolated diabetes in adolescents; INS gene mutation; a variant in the HNF1B gene in a young male with diabetes on sulfonylurea treatment.

CONCLUSION

In our cohort, the availability of an NGS panel for MD was determined for the correct identification of MD subtypes in six patients with MODY-X. Our study underlines how a precise diagnosis utilizing NGS may have an impact on the management of different forms of MODY and, thus, lead to a tailored treatment and enable genetic counselling of other family members.

Clinical and genetic characterization and long-term evaluation of individuals with maturity-onset diabetes of the young (MODY): The journey towards appropriate treatment

AIMS

To describe the clinical and genetic characteristics and long-term follow-up of a cohort with maturity-onset diabetes of the young (MODY), and to evaluate how molecular diagnosis impacted on treatment.

METHODS

A large observational, retrospective, cohort study included individuals referred to the University of São Paulo's Monogenic Diabetes Unit between 2011 and 2020. Comprehensive clinical and genetic evaluations were performed.

RESULTS

Overall, 228 individuals (190 GCK-MODY and 38 HNF1A-MODY) were enrolled. Sixty-two different GCK gene mutations (5 novel) and 17 HNF1A gene mutations (2 novel) were found. Data were available on treatment status for 76 index individuals with GCK-MODY. Before molecular diagnosis, nutritional intervention alone was used in 41 cases (53.9%). After molecular diagnosis, this number increased to 72 (94.8%). Glycated haemoglobin (HbA_1c) remained stable over the 6-year follow-up period: 6.5% (47 mmol/mol) at the first and 6.3% (45 mmol/mol) at the final visit (p = 0.056). Prior to molecular diagnosis, 7/21 (33.3%) HNF1A-MODY individuals were using sulfonylurea compared to 17/21 (81%) after testing. After a median of 5 years on sulfonylureas, HbA_1c values improved from 7.5% (58 mmol/mol) to 6.5% (48 mmol/mol) (p = 0.006).

CONCLUSIONS

Molecular diagnosis resulted in appropriate adjustment of treatment in approximately 80% of participants with GCK-MODY or HNF1A-MODY.

Identification of Variants Responsible for Monogenic Forms of Diabetes in Brazil

Monogenic forms of diabetes mellitus may affect a significant number of patients of this disease, and it is an important molecular cause to be investigated. However, studies of the genetic causes of monogenic diabetes, especially in populations with mixed ethnic backgrounds, such as the one in Brazil, are scarce. The aim of this study was to screen several genes associated with monogenic diabetes in fifty-seven Brazilian patients with recurrence of the disease in their families and thirty-four relatives. Inclusion criteria were: Age of onset ≤ 40 years old, BMI < 30 kg/m², at least two affected generations and negative anti-GAD and anti-IA2 antibodies. MODY genes HNF4A, GCK, HNF1A, HNF1B, NEUROD1, KLF11, PAX4, INS, KCNJ11, and MT-TL1 were sequenced by Sanger sequencing. We identified a total of 20 patients with variants, 13 GCK-MODY, four HNF1A-MODY, and one variant in each of the following genes, HNF4A, HNF1B and MT-TL1. Segregation analysis was performed in 13 families. Four variants were novel, two in GCK (p.(Met115Val) [c.343A>G] and p.(Asp365GlufsTer95) [c.1094_1095insGCGA]) and two in HNF1A (p.(Tyr163Ter) [c.489C>G] and p.(Val380CysfsTer39) [c.1136_1137insC]). Here we highlight the importance of screening for monogenic diabetes in admixed populations.

Screening for extremely rare pathogenic variants of monogenic diabetes using targeted panel sequencing

AIMS

Maturity-onset diabetes of the young (MODY) is one of the rare monogenic forms of diabetes. To date, about 12 genes in the scientific literature are closely related to the occurrence of the disease phenotype. However, there is still a high prevalence of undiagnosed cases of so-called MODY-X whose genetic background is still unknown.

METHODS

We performed tNGS for 523 patients with suspected MODY. Next 357 selected patients, in whom no damaging variants were found in 12 major genes causing MODY, were screened for the presence of pathogenic variants in four candidate genes (MNX1, RFX6, NKX2.2, and NKX6.1). All data were generated in one tNGS sequencing reaction and confirmed by Sanger sequencing.

RESULTS

In total, we selected five potentially damaging variants, in eight patients, in RFX6, NKX2.2, and NKX6.1 genes. Four of them have never been described in literature before. The frequency of occurrence of two of them in the RFX6 gene significantly differed in relation to the healthy population. The analysis of segregation in the family did not reveal that they were the only cause of the disease phenotype.

CONCLUSIONS

The very-rare variants indicated in this study show that this type of research on large population groups may help in the future for better understanding and more accurate diagnostics of extremely rare forms of MODY.

Genetic variants of ABCC8 and phenotypic features in Chinese early onset diabetes

BACKGROUND

ABCC8 variants cause neonatal diabetes, maturity onset diabetes of the young (MODY), and hyperinsulinemic hypoglycemia because of activating or inactivating variants. In this study we used targeted exon sequencing to investigate genetic variants of ABCC8 and phenotypic features in Chinese patients with early onset diabetes (EOD).

METHODS

A cross-sectional study of 543 Chinese patients with EOD was recruited and the exons of them were conducted targeted sequencing. The pathogenicity of ABCC8 variants was defined according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology guideline. The phenotypes of patients owing to ABCC8 variants (ABCC8-MODY) were characterized.

RESULTS

Among the 543 participants, eight (1.5%) patients with ABCC8-MODY were identified. They harbored eight missense ABCC8 variants (p.R306C, p.E1326K, and p.R1379H, previously reported; p.R298C, p.F1176C, p.R1221W, p.K1358R, and p.I1404V) classified as likely pathogenic. Two family members with ABCC8-MODY were also confirmed. The average diagnosed age of the 10 patients was 26.8 ± 12.9 years. The majority of them had unsatisfactory glucose control, 80% of them had diabetic kidney disease, and neurological features were not observed.

CONCLUSION

Using targeted exon sequencing followed by pathogenicity analysis, we could be able to make genetic diagnoses for eight (1.5%) patients with ABCC8-MODY. The phenotype was variable with higher risk of diabetic microvascular complications. Genetic diagnosis is conducive for facilitating the personalized treatment of ABCC8-MODY.

MODY probability calculator for GCK and HNF1A screening in a multiethnic background population

Objective We aimed to identify the frequency of monogenic diabetes, which is poorly studied in multiethnic populations, due to GCK or HNF1A mutations in patients with suggestive clinical characteristics from the Brazilian population, as well as investigate if the MODY probability calculator (MPC) could help patients with their selection. Subjects and methods Inclusion criteria were patients with DM diagnosed before 35 years; body mass index < 30 kg/m2; negative autoantibodies; and family history of DM in two or more generations. We sequenced HNF1A in 27 patients and GCK in seven subjects with asymptomatic mild fasting hyperglycemia. In addition, we calculated MODY probability with MPC. Results We identified 11 mutations in 34 patients (32.3%). We found three novel mutations. In the GCK group, six cases had mutations (85.7%), and their MODY probability on MPC was higher than 50%. In the HNF1A group, five of 27 individuals had mutations (18.5%). The MPC was higher than 75% in 11 subjects (including all five cases with HNF1A mutations). Conclusion Approximately one third of the studied patients have GCK or HNF1A mutations. Inclusion criteria included efficiency in detecting patients with GCK mutations but not for HNF1A mutations (< 20%). MPC was helpful in narrowing the number of candidates for HNF1A screening.

Targeted sequencing identifies novel variants in common and rare MODY genes

BACKGROUND

Maturity-onset diabetes of the young (MODY) is a form of monogenic diabetes with autosomal dominant inheritance. To date, mutations in 11 genes have been frequently associated with this phenotype. In Brazil, few cohorts have been screened for MODY, all using a candidate gene approach, with a high prevalence of undiagnosed cases (MODY-X).

METHODS

We conducted a next-generation sequencing target panel (tNGS) study to investigate, for the first time, a Brazilian cohort of MODY patients with a negative prior genetic analysis. One hundred and two patients were selected, of which 26 had an initial clinical suspicion of MODY-GCK and 76 were non-GCK MODY.

RESULTS

After excluding all benign and likely benign variants and variants of uncertain significance, we were able to assign a genetic cause for 12.7% (13/102) of the probands. Three rare MODY subtypes were identified (PDX1/NEUROD1/ABCC8), and eight variants had not been previously described/mapped in genomic databases. Important clinical findings were evidenced in some cases after genetic diagnosis, such as MODY-PDX1/HNF1B.

CONCLUSION

A multiloci genetic approach allowed the identification of rare MODY subtypes, reducing the large percentage of MODY-X in Brazilian cases and contributing to a better clinical, therapeutic, and prognostic characterization of these rare phenotypes.

Identification and functional analysis of GCK gene mutations in 12 Chinese families with hyperglycemia

AIMS/INTRODUCTION

To investigate the clinical and genetic characteristics of Chinese patients with a phenotype consistent with maturity-onset diabetes of the young type 2 and explore the pathogenic mechanism of their hyperglycemia.

MATERIALS AND METHODS

We studied 12 probands and their extended families referred to our center for screening mutations in the glucokinase gene (GCK). Clinical data were collected and genetic analysis was carried out. The recombinant wild-type and mutant glucokinase were generated in Escherichia coli. The kinetic parameters and thermal stability of the enzymes were determined in vitro.

RESULTS

In the 12 families, 11 GCK mutations (R43C, T168A, K169N, R191W, Y215X, E221K, M235T, R250H, W257X, G261R and A379E) and one variant of uncertain significance (R275H) were identified. R191W was detected in two unrelated families. Of the 11 GCK mutations, three mutations (c.507G>C, K169N; c.645C>A, Y215X; c.771G>A, W257X; NM_000162.3, NP_000153.1) are novel. Basic kinetics analysis explained the pathogenicity of the five mutants (R43C, K169N, R191W, E221K and A379E), which showed reduced enzyme activity with relative activity indexes between ~0.001 and 0.5 compared with the wild-type (1.0). In addition, the thermal stabilities of these five mutants were also decreased to varying degrees. However, for R250H and R275H, there was no significant difference in the enzyme activity and thermal stability between the mutants and the wild type.

CONCLUSIONS

We have identified 11 GCK mutations and one variant of uncertain significance in 12 Chinese families with hyperglycemia. For five GCK mutations (R43C, K169N, R191W, E221K and A379E), the changes in enzyme kinetics and thermostability might be the pathogenic mechanisms by which mutations cause hyperglycemia.

Identifying Pathogenic Variants of Monogenic Diabetes Using Targeted Panel Sequencing in an East Asian Population

PURPOSE

Monogenic diabetes is a specific type of diabetes in which precision medicine could be applied. In this study, we used targeted panel sequencing to investigate pathogenic variants in Korean patients clinically suspected to have monogenic diabetes.

METHODS

The eligibility criteria for inclusion were non-type 1 diabetes patients with an age of onset ≤ 30 years and a BMI (body mass index) ≤ 30 kg/m2. Among the 2,090 non-type 1 diabetes patients, 109 were suspected to have monogenic diabetes and subjected to genetic testing. We analyzed 30 monogenic diabetes genes using targeted panel sequencing. The pathogenicity of the genetic variants was evaluated according to the American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines.

RESULTS

Among the 109 suspected monogenic diabetes patients, 23 (21.1%) patients harbored pathogenic/likely pathogenic variants. A total of 14 pathogenic/likely pathogenic variants of common maturity onset diabetes of the young (MODY) genes were identified in GCK, HNF1A, HNF4A, and HNF1B. Other pathogenic/likely pathogenic variants were identified in WFS1, INS, ABCC8 and FOXP3. The mitochondrial DNA 3243 A>G variant was identified in five participants. Patients with pathogenic/likely pathogenic variants had a significantly higher MODY probability, a lower BMI, and a lower C-peptide level than those without pathogenic/likely pathogenic variants (P=0.007, P=0.001, and P=0.012, respectively).

CONCLUSIONS

Using targeted panel sequencing followed by pathogenicity evaluation, we were able to make molecular genetic diagnoses for 23 (21.1%) suspected monogenic diabetes patients. Lower BMI, higher MODY probability, and lower C-peptide levels were characteristics of these participants.

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

Guideline-based and bioinformatic reassessment of lesion-associated gene and variant pathogenicity in focal human epilepsies

OBJECTIVE

Increasing availability of surgically resected brain tissue from patients with focal epilepsy and focal cortical dysplasia or low-grade glioneuronal tumors has fostered large-scale genetic examination. However, assessment of pathogenicity of germ line and somatic variants remains difficult. Here, we present a state-of-the-art evaluation of reported genes and variants associated with epileptic brain lesions.

METHODS

We critically reevaluated the pathogenicity for all neuropathology-associated variants reported to date in the PubMed and ClinVar databases, including 101 neuropathology-associated missense variants encompassing 11 disease-related genes. We assessed gene variant tolerance and classified all identified missense variants according to guidelines from the American College of Medical Genetics and Genomics (ACMG). We further extended the bioinformatic variant prediction by introducing a novel gene-specific deleteriousness ranking for prediction scores.

RESULTS

Application of ACMG guidelines and in silico gene variant tolerance analysis classified only seven of 11 genes to be likely disease-associated according to the reported disease mechanism, whereas 61 (60.4%) of 101 variants of those genes were classified as of uncertain significance, 37 (36.6%) as being likely pathogenic, and 3 (3%) as being pathogenic.

SIGNIFICANCE

We concluded that the majority of neuropathology-associated variants reported to date do not have enough evidence to be classified as pathogenic. Interpretation of lesion-associated variants remains challenging, and application of current ACMG guidelines is recommended for interpretation and prediction.

CardioVAI: An automatic implementation of ACMG-AMP variant interpretation guidelines in the diagnosis of cardiovascular diseases

Variant interpretation for the diagnosis of genetic diseases is a complex process. The American College of Medical Genetics and Genomics, with the Association for Molecular Pathology, have proposed a set of evidence-based guidelines to support variant pathogenicity assessment and reporting in Mendelian diseases. Cardiovascular disorders are a field of application of these guidelines, but practical implementation is challenging due to the genetic disease heterogeneity and the complexity of information sources that need to be integrated. Decision support systems able to automate variant interpretation in the light of specific disease domains are demanded. We implemented CardioVAI (Cardio Variant Interpreter), an automated system for guidelines based variant classification in cardiovascular-related genes. Different omics-resources were integrated to assess pathogenicity of every genomic variant in 72 cardiovascular diseases related genes. We validated our method on benchmark datasets of high-confident assessed variants, reaching pathogenicity and benignity concordance up to 83 and 97.08%, respectively. We compared CardioVAI to similar methods and analyzed the main differences in terms of guidelines implementation. We finally made available CardioVAI as a web resource (http://cardiovai.engenome.com/) that allows users to further specialize guidelines recommendations.