An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

Leveraging multiple approaches for the detection of pathogenic deep intronic variants in developmental and epileptic encephalopathies: A case report.

About 50% of individuals with developmental and epileptic encephalopathies (DEEs) are unsolved following genetic testing. Deep intronic variants, defined as >100 bp from exon-intron junctions, contribute to disease by affecting the splicing of mRNAs in clinically relevant genes. Identifying deep intronic pathogenic variants is challenging and resource intensive, and interpretation is difficult due to limited functional annotations. We aimed to identify deep intronic variants in individuals suspected to have unsolved single gene DEEs. In a research cohort of unsolved cases of DEEs, we searched for children with a DEE syndrome predominantly caused by variants in specific genes in >80% of described cases. We identified two children with Dravet syndrome and one individual with classic lissencephaly. Multiple sequencing and bioinformatics strategies were employed to interrogate intronic regions in SCN1A and PAFAH1B1. A novel de novo deep intronic 12 kb deletion in PAFAH1B1 was identified in the individual with lissencephaly. We showed experimentally that the deletion disrupts mRNA splicing, which results in partial intron retention after exon 2 and disruption of the highly conserved LisH motif. We demonstrate that targeted interrogation of deep intronic regions using multiple genomics technologies, coupled with functional analysis, can reveal hidden causes of unsolved monogenic DEE syndromes. PLAIN LANGUAGE SUMMARY: Deep intronic variants can cause disease by affecting the splicing of mRNAs in clinically relevant genes. A deep intronic deletion that caused abnormal splicing of the PAFAH1B1 gene was identified in a patient with classic lissencephaly. Our findings reinforce that targeted interrogation of deep intronic regions and functional analysis can reveal hidden causes of unsolved epilepsy syndromes.

Diagnostic Utility of Genome-wide DNA Methylation Analysis in Genetically Unsolved Developmental and Epileptic Encephalopathies and Refinement of a CHD2 Episignature.

Sequence-based genetic testing currently identifies causative genetic variants in ∼50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but remain unstudied in DEEs. Rare epigenetic variations ("epivariants") can drive disease by modulating gene expression at single loci, whereas genome-wide DNA methylation changes can result in distinct "episignature" biomarkers for monogenic disorders in a growing number of rare diseases. Here, we interrogate the diagnostic utility of genome-wide DNA methylation array analysis on peripheral blood samples from 516 individuals with genetically unsolved DEEs who had previously undergone extensive genetic testing. We identified rare differentially methylated regions (DMRs) and explanatory episignatures to discover causative and candidate genetic etiologies in 10 individuals. We then used long-read sequencing to identify DNA variants underlying rare DMRs, including one balanced translocation, three CG-rich repeat expansions, and two copy number variants. We also identify pathogenic sequence variants associated with episignatures; some had been missed by previous exome sequencing. Although most DEE genes lack known episignatures, the increase in diagnostic yield for DNA methylation analysis in DEEs is comparable to the added yield of genome sequencing. Finally, we refine an episignature for CHD2 using an 850K methylation array which was further refined at higher CpG resolution using bisulfite sequencing to investigate potential insights into CHD2 pathophysiology. Our study demonstrates the diagnostic yield of genome-wide DNA methylation analysis to identify causal and candidate genetic causes as ∼2% (10/516) for unsolved DEE cases.

Dyrk1a Mutations Cause Undergrowth of Cortical Pyramidal Neurons via Dysregulated Growth Factor Signaling.

BACKGROUND: Mutations in DYRK1A are a cause of microcephaly, autism spectrum disorder, and intellectual disability; however, the underlying cellular and molecular mechanisms are not well understood. METHODS: We generated a conditional mouse model using Emx1-cre, including conditional heterozygous and homozygous knockouts, to investigate the necessity of Dyrk1a in the cortex during development. We used unbiased, high-throughput phosphoproteomics to identify dysregulated signaling mechanisms in the developing Dyrk1a mutant cortex as well as classic genetic modifier approaches and pharmacological therapeutic intervention to rescue microcephaly and neuronal undergrowth caused by Dyrk1a mutations. RESULTS: We found that cortical deletion of Dyrk1a in mice causes decreased brain mass and neuronal size, structural hypoconnectivity, and autism-relevant behaviors. Using phosphoproteomic screening, we identified growth-associated signaling cascades dysregulated upon Dyrk1a deletion, including TrkB-BDNF (tyrosine receptor kinase B-brain-derived neurotrophic factor), an important regulator of ERK/MAPK (extracellular signal-regulated kinase/mitogen-activated protein kinase) and mTOR (mammalian target of rapamycin) signaling. Genetic suppression of Pten or pharmacological treatment with IGF-1 (insulin-like growth factor-1), both of which impinge on these signaling cascades, rescued microcephaly and neuronal undergrowth in neonatal mutants. CONCLUSIONS: Altogether, these findings identify a previously unknown mechanism through which Dyrk1a mutations disrupt growth factor signaling in the developing brain, thus influencing neuronal growth and connectivity. Our results place DYRK1A as a critical regulator of a biological pathway known to be dysregulated in humans with autism spectrum disorder and intellectual disability. In addition, these data position Dyrk1a within a larger group of autism spectrum disorder/intellectual disability risk genes that impinge on growth-associated signaling cascades to regulate brain size and connectivity, suggesting a point of convergence for multiple autism etiologies.