We’re proud to see our customers continuing to push the boundaries of genetic research and molecular diagnostics. Our September publications showcase how advanced genomic technologies can help uncover disease-causing variants, deepen our understanding of rare disease biology, and support the development of more comprehensive diagnostic approaches.
From identifying recurrent and novel variants in congenital hereditary endothelial dystrophy, to revealing new lipid signatures in glycogen storage disease, and validating an integrated DNA and RNA sequencing panel for thyroid pathology, these studies highlight the diverse ways researchers are translating genomic data into meaningful scientific insights. We’re excited to celebrate these contributions and the role our technology plays in helping researchers turn complex genomic data into actionable discoveries.
Complete SLC4A11 Detection in Saudi Congenital Hereditary Endothelial Dystrophy: A Transmembrane Glycine Hotspot and a Recurrent Splice Donor Allele in Consanguineous Patients
Background: In Saudi Arabia, congenital hereditary endothelial dystrophy (CHED) is a rare autosomal recessive corneal disorder strongly associated with SLC4A11 mutations, with consanguinity contributing to population-specific pathogenic variants and highlighting the importance of genetic testing for accurate diagnosis.
Objective: This study investigates a larger cohort to identify recurrent pathogenic variants that could support faster, targeted diagnosis.
Subjects and Methods: The study prospectively enrolled 47 Saudi patients from related families with clinically diagnosed CHED. Researchers used whole-exome sequencing followed by Sanger confirmation and family segregation analysis to identify and classify pathogenic variants in SLC4A11, supported by population databases, computational prediction tools, and ACMG/AMP guidelines.
Results: All 47 patients carried homozygous SLC4A11 variants, with 20 unique variants identified—including three novel variants—while the cohort showed considerable clinical variability and was dominated by recurrent missense and splice-site mutations.
Conclusions: Researchers identified 20 homozygous SLC4A11 variants, including three novel variants, with recurrent mutations concentrated in key structural and splice-site hotspots, supporting a targeted Sanger-based first-line diagnostic approach for this population.
How VarSeq Was Used: “Variants were annotated and filtered using (Golden Helix VarSeq), drawing on clinical information, population frequency databases, disease databases, and in silico prediction tools, with gene and variant nomenclature following the HUGO Gene Nomenclature Committee (HGNC) and the Human Genome Variation Society (HGVS), respectively. Candidate variants were assessed for pathogenicity using PolyPhen-2, SIFT, and MutationTaster, and their allele frequencies were checked against gnomAD, the Exome Sequencing Project (ESP), the 1000 Genomes Project, and a Saudi population database. Variants were then classified as pathogenic, likely pathogenic, of uncertain significance, likely benign, or benign according to the American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) 2015 guidelines and the recommendations of the ClinGen Sequence Variation Interpretation Working Group (https://clinicalgenome.org).”
Citation: Abu-Amero, K. K., Malik, R., AlHilali, S. M., & Jomar, D. E. (2026). Complete SLC4A11 Detection in Saudi Congenital Hereditary Endothelial Dystrophy: A Transmembrane Glycine Hotspot and a Recurrent Splice Donor Allele in Consanguineous Patients. International Journal of Molecular Sciences, 27(16), 7220. https://doi.org/10.3390/ijms27167220
Systematic Genetic, Biochemical, and Lipidomic Characterization of GSD III Fibroblast Models Reveals Mis-annotation and Defines a Conserved Disease Lipid Signature
Background: GSD III is a rare inherited disorder caused by pathogenic variants in the AGL gene that impair glycogen breakdown, leading to abnormal glycogen accumulation and progressive liver and muscle complications, while genetic and cellular variability can complicate disease modeling and research.
Objective: This study systematically characterized GSD III fibroblast models using genomic, biochemical, and lipidomic analyses, identifying confirmed disease models, a mis-annotated cell line with a similar lipid signature, and a new immortalized AGL-deficient model to support disease research and therapeutic development.
Subjects and Methods: Researchers evaluated nine GSD III-designated and four control fibroblast lines using whole-genome sequencing, protein and glycogen assays, and lipidomic profiling. Candidate variants were validated and analyzed alongside metabolic phenotypes to genetically and functionally characterize the cell models.
Results: Genomic analysis confirmed AGL mutations in eight of nine GSD III fibroblast lines, while one was found to be misclassified with a different metabolic disorder. The confirmed GSD III models showed loss of GDE protein, impaired glycogen breakdown, and a distinct lipidomic signature that was also reproduced in a newly generated AGL-deficient model.
Conclusions: The study identified four well-characterized patient fibroblast lines and a controlled AGL−/− model as robust tools for studying GSD III disease mechanisms, metabolic signatures, and potential therapies.
How VarSeq Was Used: “Variant Calling and Annotation: Sequence reads were processed using the Genome Analysis Toolkit (GATK; Broad Institute) for variant calling. Variants were subsequently annotated using the VarSeq® platform v2.6.2 (Golden Helix, Inc., Bozeman, MT, www.goldenhelix.com).”
Citation: K.A. Becker, C.K. Pathmasiri, E.S. Phillippi, K. Casazza, D.W. Todd, D.A. Weinstein, K.L. Ode, A.R. Calhoun, S.M. Cologna, M.L. Schultz. (2026) Systematic genetic, biochemical, and lipidomic characterization of GSD III fibroblast models reveals mis-annotation and defines a conserved disease lipid signature, Molecular Genetics and Metabolism, https://doi.org/10.1016/j.ymgme.2026.110249
Design and Analytical Validation of Nexthyro 2.0, a Custome Next-Generation Sequencing Panel Integrating DNA Mutation Profiling and RNA Fusion Detection for Routine Thyroid Pathology Specimens
Background: Molecular testing has become an important tool for resolving indeterminate thyroid FNA diagnoses and guiding treatment, particularly for advanced thyroid cancers. However, existing commercial panels may have limited sensitivity for small FNA samples, highlighting the need for thyroid-specific NGS panels that integrate comprehensive DNA and RNA analysis with sample adequacy assessment.
Objective: The researchers developed an updated custom NGS panel that expands DNA variant detection and adds RNA-based fusion detection and expression controls to assess sample cellularity. They evaluated its performance across cell lines, FFPE tissue, and thyroid FNA samples and examined how molecular findings correlated with tumor histology and cytological classifications.
Subjects and Methods: Researchers designed and analytically validated Nexthyro 2.0, a thyroid-specific NGS panel covering 285 DNA alterations across 21 genes, 171 RNA fusion partners, and gene-expression controls for assessing sample adequacy. The panel was tested using thyroid cancer cell lines, 124 FFPE tissue samples, and 43 FNA specimens on an automated Genexus workflow to evaluate its sensitivity and performance in clinically relevant samples.
Results: Nexthyro 2.0 demonstrated high analytical sensitivity, detecting variants down to 0.5% VAF, and successfully analyzed 95% of FNA samples and all FFPE specimens. The panel identified clinically relevant alterations across diverse thyroid tumors, with BRAF-like alterations enriched in papillary thyroid carcinoma and RAS-like alterations common in indeterminate FNA samples, demonstrating its potential utility in routine thyroid diagnostics.
Conclusions: Nexthyro 2.0 combines DNA mutation and RNA fusion detection with high sensitivity across thyroid tissue and FNA samples, supporting its potential for routine molecular testing and future liquid biopsy applications.
How GenomeBrowse Was Used: “Data analysis was performed using proprietary Genexus software (6.8.4.0). Alterations detected by the assay were annotated in the software, which lists all identified variants, and subsequently confirmed by visual inspection of BAM files in hotspot regions using Golden Helix Genome Browser (v3.1.0; Bozeman, MT, USA). Variant interpretation involved manual curation integrating ClinVar, OncoKB, and COSMIC databases.”
Citation: Nacchio, M., Carillo, A.M., Di Giovanni, D. et al. Design and analytical validation of Nexthyro 2.0, a custom next-generation sequencing panel integrating DNA mutation profiling and RNA fusion detection for routine thyroid pathology specimens. Virchows Arch (2026). https://doi.org/10.1007/s00428-026-04659-z
