A recurrent de novo MAX p.Arg60Gln variant causes a syndromic overgrowth disorder through differential expression of c-Myc target genes.

Cyclin D2 (CCND2) stabilization underpins a range of macrocephaly-associated disorders through mutation of CCND2 or activating mutations in upstream genes encoding PI3K-AKT pathway components. Here, we describe three individuals with overlapping macrocephaly-associated phenotypes who carry the same recurrent de novo c.179G>A (p.Arg60Gln) variant in Myc-associated factor X (MAX). The mutation, located in the b-HLH-LZ domain, causes increased intracellular CCND2 through increased transcription but it does not cause stabilization of CCND2. We show that the purified b-HLH-LZ domain of MAXArg60Gln (Max∗Arg60Gln) binds its target E-box sequence with a lower apparent affinity. This leads to a more efficient heterodimerization with c-Myc resulting in an increase in transcriptional activity of c-Myc in individuals carrying this mutation. The recent development of Omomyc-CPP, a cell-penetrating b-HLH-LZ-domain c-Myc inhibitor, provides a possible therapeutic option for MAXArg60Gln individuals, and others carrying similar germline mutations resulting in dysregulated transcriptional c-Myc activity.

Identification of a DNA methylation episignature for recurrent constellations of embryonic malformations.

The term "recurrent constellations of embryonic malformations" (RCEM) is used to describe a number of multiple malformation associations that affect three or more body structures. The causes of these disorders are currently unknown, and no diagnostic marker has been identified. Consequently, providing a definitive diagnosis in suspected individuals is challenging. In this study, genome-wide DNA methylation analysis was conducted on DNA samples obtained from the peripheral blood of 53 individuals with RCEM characterized by clinical features recognized as VACTERL and/or oculoauriculovertebral spectrum association. We identified a common DNA methylation episignature in 40 out of the 53 individuals. Subsequently, a sensitive and specific binary classifier was developed based on the DNA methylation episignature. This classifier can facilitate the use of RCEM episignature as a diagnostic biomarker in a clinical setting. The study also investigated the functional correlation of RCEM DNA methylation relative to other genetic disorders with known episignatures, highlighting the common genomic regulatory pathways involved in the pathophysiology of RCEM.

Refining the 9q34.3 microduplication syndrome reveals mild neurodevelopmental features associated with a distinct global DNA methylation profile.

Precise regulation of gene expression is important for correct neurodevelopment. 9q34.3 deletions affecting the EHMT1 gene result in a syndromic neurodevelopmental disorder named Kleefstra syndrome. In contrast, duplications of the 9q34.3 locus encompassing EHMT1 have been suggested to cause developmental disorders, but only limited information has been available. We have identified 15 individuals from 10 unrelated families, with 9q34.3 duplications <1.5 Mb in size, encompassing EHMT1 entirely. Clinical features included mild developmental delay, mild intellectual disability or learning problems, autism spectrum disorder, and behavior problems. The individuals did not consistently display dysmorphic features, congenital anomalies, or growth abnormalities. DNA methylation analysis revealed a weak DNAm profile for the cases with 9q34.3 duplication encompassing EHMT1, which could segregate the majority of the affected cases from controls. This study shows that individuals with 9q34.3 duplications including EHMT1 gene present with mild non-syndromic neurodevelopmental disorders and DNA methylation changes different from Kleefstra syndrome.

Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.

STUDY QUESTION: Can generative artificial intelligence (AI) models produce high-fidelity images of human blastocysts? SUMMARY ANSWER: Generative AI models exhibit the capability to generate high-fidelity human blastocyst images, thereby providing substantial training datasets crucial for the development of robust AI models. WHAT IS KNOWN ALREADY: The integration of AI into IVF procedures holds the potential to enhance objectivity and automate embryo selection for transfer. However, the effectiveness of AI is limited by data scarcity and ethical concerns related to patient data privacy. Generative adversarial networks (GAN) have emerged as a promising approach to alleviate data limitations by generating synthetic data that closely approximate real images. STUDY DESIGN, SIZE, DURATION: Blastocyst images were included as training data from a public dataset of time-lapse microscopy (TLM) videos (n = 136). A style-based GAN was fine-tuned as the generative model. PARTICIPANTS/MATERIALS, SETTING, METHODS: We curated a total of 972 blastocyst images as training data, where frames were captured within the time window of 110-120 h post-insemination at 1-h intervals from TLM videos. We configured the style-based GAN model with data augmentation (AUG) and pretrained weights (Pretrained-T: with translation equivariance; Pretrained-R: with translation and rotation equivariance) to compare their optimization on image synthesis. We then applied quantitative metrics including Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) to assess the quality and fidelity of the generated images. Subsequently, we evaluated qualitative performance by measuring the intelligence behavior of the model through the visual Turing test. To this end, 60 individuals with diverse backgrounds and expertise in clinical embryology and IVF evaluated the quality of synthetic embryo images. MAIN RESULTS AND THE ROLE OF CHANCE: During the training process, we observed consistent improvement of image quality that was measured by FID and KID scores. Pretrained and AUG + Pretrained initiated with remarkably lower FID and KID values compared to both Baseline and AUG + Baseline models. Following 5000 training iterations, the AUG + Pretrained-R model showed the highest performance of the evaluated five configurations with FID and KID scores of 15.2 and 0.004, respectively. Subsequently, we carried out the visual Turing test, such that IVF embryologists, IVF laboratory technicians, and non-experts evaluated the synthetic blastocyst-stage embryo images and obtained similar performance in specificity with marginal differences in accuracy and sensitivity. LIMITATIONS, REASONS FOR CAUTION: In this study, we primarily focused the training data on blastocyst images as IVF embryos are primarily assessed in blastocyst stage. However, generation of an array of images in different preimplantation stages offers further insights into the development of preimplantation embryos and IVF success. In addition, we resized training images to a resolution of 256 × 256 pixels to moderate the computational costs of training the style-based GAN models. Further research is needed to involve a more extensive and diverse dataset from the formation of the zygote to the blastocyst stage, e.g. video generation, and the use of improved image resolution to facilitate the development of comprehensive AI algorithms and to produce higher-quality images. WIDER IMPLICATIONS OF THE FINDINGS: Generative AI models hold promising potential in generating high-fidelity human blastocyst images, which allows the development of robust AI models as it can provide sufficient training datasets while safeguarding patient data privacy. Additionally, this may help to produce sufficient embryo imaging training data with different (rare) abnormal features, such as embryonic arrest, tripolar cell division to avoid class imbalances and reach to even datasets. Thus, generative models may offer a compelling opportunity to transform embryo selection procedures and substantially enhance IVF outcomes. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by a Horizon 2020 innovation grant (ERIN, grant no. EU952516) and a Horizon Europe grant (NESTOR, grant no. 101120075) of the European Commission to A.S. and M.Z.E., the Estonian Research Council (grant no. PRG1076) to A.S., and the EVA (Erfelijkheid Voortplanting & Aanleg) specialty program (grant no. KP111513) of Maastricht University Medical Centre (MUMC+) to M.Z.E. TRIAL REGISTRATION NUMBER: Not applicable.

Clinical Guideline for Preimplantation Genetic Testing in Inherited Cardiac Diseases.

BACKGROUND: Preimplantation genetic testing (PGT) is a reproductive technology that selects embryos without (familial) genetic variants. PGT has been applied in inherited cardiac disease and is included in the latest American Heart Association/American College of Cardiology guidelines. However, guidelines selecting eligible couples who will have the strongest risk reduction most from PGT are lacking. We developed an objective decision model to select eligibility for PGT and compared its results with those from a multidisciplinary team. METHODS: All couples with an inherited cardiac disease referred to the national PGT center were included. A multidisciplinary team approved or rejected the indication based on clinical and genetic information. We developed a decision model based on published risk prediction models and literature, to evaluate the severity of the cardiac phenotype and the penetrance of the familial variant in referred patients. The outcomes of the model and the multidisciplinary team were compared in a blinded fashion. RESULTS: Eighty-three couples were referred for PGT (1997-2022), comprising 19 different genes for 8 different inherited cardiac diseases (cardiomyopathies and arrhythmias). Using our model and proposed cutoff values, a definitive decision was reached for 76 (92%) couples, aligning with 95% of the multidisciplinary team decisions. In a prospective cohort of 11 couples, we showed the clinical applicability of the model to select couples most eligible for PGT. CONCLUSIONS: The number of PGT requests for inherited cardiac diseases increases rapidly, without the availability of specific guidelines. We propose a 2-step decision model that helps select couples with the highest risk reduction for cardiac disease in their offspring after PGT.

Genome sequencing as a generic diagnostic strategy for rare disease.

BACKGROUND: To diagnose the full spectrum of hereditary and congenital diseases, genetic laboratories use many different workflows, ranging from karyotyping to exome sequencing. A single generic high-throughput workflow would greatly increase efficiency. We assessed whether genome sequencing (GS) can replace these existing workflows aimed at germline genetic diagnosis for rare disease. METHODS: We performed short-read GS (NovaSeq™6000; 150 bp paired-end reads, 37 × mean coverage) on 1000 cases with 1271 known clinically relevant variants, identified across different workflows, representative of our tertiary diagnostic centers. Variants were categorized into small variants (single nucleotide variants and indels < 50 bp), large variants (copy number variants and short tandem repeats) and other variants (structural variants and aneuploidies). Variant calling format files were queried per variant, from which workflow-specific true positive rates (TPRs) for detection were determined. A TPR of ≥ 98% was considered the threshold for transition to GS. A GS-first scenario was generated for our laboratory, using diagnostic efficacy and predicted false negative as primary outcome measures. As input, we modeled the diagnostic path for all 24,570 individuals referred in 2022, combining the clinical referral, the transition of the underlying workflow(s) to GS, and the variant type(s) to be detected. RESULTS: Overall, 95% (1206/1271) of variants were detected. Detection rates differed per variant category: small variants in 96% (826/860), large variants in 93% (341/366), and other variants in 87% (39/45). TPRs varied between workflows (79-100%), with 7/10 being replaceable by GS. Models for our laboratory indicate that a GS-first strategy would be feasible for 84.9% of clinical referrals (750/883), translating to 71% of all individuals (17,444/24,570) receiving GS as their primary test. An estimated false negative rate of 0.3% could be expected. CONCLUSIONS: GS can capture clinically relevant germline variants in a 'GS-first strategy' for the majority of clinical indications in a genetics diagnostic lab.

Unravelling undiagnosed rare disease cases by HiFi long-read genome sequencing.

Solve-RD is a pan-European rare disease (RD) research program that aims to identify disease-causing genetic variants in previously undiagnosed RD families. We utilised 10-fold coverage HiFi long-read sequencing (LRS) for detecting causative structural variants (SVs), single nucleotide variants (SNVs), insertion-deletions (InDels), and short tandem repeat (STR) expansions in extensively studied RD families without clear molecular diagnoses. Our cohort includes 293 individuals from 114 genetically undiagnosed RD families selected by European Rare Disease Network (ERN) experts. Of these, 21 families were affected by so-called 'unsolvable' syndromes for which genetic causes remain unknown, and 93 families with at least one individual affected by a rare neurological, neuromuscular, or epilepsy disorder without genetic diagnosis despite extensive prior testing. Clinical interpretation and orthogonal validation of variants in known disease genes yielded thirteen novel genetic diagnoses due to de novo and rare inherited SNVs, InDels, SVs, and STR expansions. In an additional four families, we identified a candidate disease-causing SV affecting several genes including an MCF2 / FGF13 fusion and PSMA3 deletion. However, no common genetic cause was identified in any of the 'unsolvable' syndromes. Taken together, we found (likely) disease-causing genetic variants in 13.0% of previously unsolved families and additional candidate disease-causing SVs in another 4.3% of these families. In conclusion, our results demonstrate the added value of HiFi long-read genome sequencing in undiagnosed rare diseases.

Lessons learned from rapid exome sequencing for 575 critically ill patients across the broad spectrum of rare disease.

Introduction: Rapid exome sequencing (rES) has become the first-choice genetic test for critically ill patients, mostly neonates, young infants, or fetuses in prenatal care, in time-sensitive situations and when it is expected that the genetic test result may guide clinical decision making. The implementation of rES has revolutionized medicine by enabling timely identification of genetic causes for various rare diseases. The utilization of rES has increasingly been recognized as an essential diagnostic tool for the identification of complex and undiagnosed genetic disorders. Methods: We conducted a retrospective evaluation of our experiences with rES performed on 575 critically ill patients from various age groups (prenatal to adulthood), over a four-year period (2016-2019). These patients presented with a wide spectrum of rare diseases, including but not limited to neurological disorders, severe combined immune deficiency, and cancer. Results: During the study period, there was a significant increase in rES referrals, with a rise from a total of two referrals in Q1-2016 to 10 referrals per week in Q4-2019. The median turnaround time (TAT) decreased from 17 to 11 days in the period 2016-2019, with an overall median TAT of 11 days (IQR 8-15 days). The overall diagnostic yield for this cohort was 30.4%, and did not significantly differ between the different age groups (e.g. adults 22.2% vs children 31.0%; p-value 0.35). However, variability in yield was observed between clinical entities: craniofacial anomalies yielded 58.3%, while for three clinical entities (severe combined immune deficiency, aneurysm, and hypogonadotropic hypogonadism) no diagnoses were obtained. Discussion: Importantly, whereas clinical significance is often only attributed to a conclusive diagnosis, we also observed impact on clinical decision-making for individuals in whom no genetic diagnosis was established. Hence, our experience shows that rES has an important role for patients of all ages and across the broad spectrum of rare diseases to impact clinical outcomes.