Biology subjects

Ljungdahl, A.

Publications and source records attributed to Ljungdahl, A..

3 recordsLinked to original sources

bioRxiv2026

Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Sequencing analyses have identified 185 ASD-associated genes, which implicate neurons, but the specific brain regions through which these neurons influence neurodevelopment remain unclear. Here, we integrate over one million single-cell RNA sequencing profiles from 20 regions of the developing human brain (4-23 post-conceptual weeks) using a new framework, STARMAPS (Sparse Task-specific Analysis for Revealing Molecular Associations in Particular Single-cell datasets). STARMAPS accounts for coordinated regional and developmental perturbations in gene expression, enabling robust cross-region comparison. We replicate prior findings that ASD-associated genes are enriched in excitatory neurons during mid-fetal development, and we extend these results to reveal distinct spatial signatures. Across 26 excitatory neuron subtypes, six clusters showed significant enrichment for ASD-associated genes. These clusters localize to both cortical and subcortical regions, including the motor, temporal, and visual cortex, as well as the thalamus and hypothalamus. Our findings support a major role for excitatory neurons across distributed brain circuits, implicating previously underappreciated subcortical structures in ASD etiology. By providing a statistically rigorous framework for spatiotemporal integration of single-cell data, STARMAPS enables refined mapping of molecular vulnerability across the developing human brain.

genomics↗

bioRxiv2025

Targeted BDNF upregulation via upstream open reading frame disruption

To understand the relative contributions of 5' UTR elements to translation, we performed a comprehensive analysis of upstream open reading frames (uORFs) across a representative 5' UTR. We selected the neurotrophin BDNF (Brain derived neurotrophic factor) as an exemplar as upregulation of this protein is a potential therapeutic approach for a plethora of neurodevelopmental, neurodegenerative, and neuropsychiatric disorder indications. Predicted uORFs were identified in 14 out of 17 BDNF RefSeq transcript isoforms, and experimentally confirmed to be exerting translation repression effects for five of these transcripts. These findings suggest that uORF elements play an important role in shaping the protein output from this locus. We explored several approaches to disrupt BDNF uORF function. Deletion of a 5' UTR exon in BDNF v11 (containing eight predicted uORFs), in order to simulate an exon skipping outcome, resulted in pronounced upregulation in a reporter construct system. This effect was found to be partially uORF-dependent, but was also dependent on the disruption of an RNA secondary structure element. However, this transcript variant was found to not be expressed in human brain. Conversely, direct disruption of a single uORF start codon in the widely expressed BDNF v4 transcript variant using an adenine base editing approach resulted in a [~]1.8-fold upregulation of endogenous BDNF protein expression in cell culture. This study describes novel BDNF regulatory mechanisms, and potential uORF-targeted modalities for therapeutic gene activation.

molecular biology↗

bioRxiv2023

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithms

Missense variants that alter a single amino acid in the encoded protein contribute to many human disorders but pose a substantial challenge in interpretation. Though these variants can be reliably identified through sequencing, distinguishing the clinically significant ones remains difficult, such that "Variants of Unknown Significance" outnumber those classified as "Pathogenic" or "Likely Pathogenic." Numerous in silico approaches have been developed to predict the functional impact of missense variants to inform clinical interpretation, the latest being AlphaMissense, which uses artificial intelligence methods trained on predicted protein structure. To independently assess the performance of AlphaMissense and 38 other predictors of missense severity, we compared predictions to data from multiplexed assays of variant effect (MAVE). MAVE experiments generate almost every possible individual amino acid change in a gene and measure their functional impact using a high-throughput assay. Assessing 17,696 variants across five genes (DDX3X, MSH2, PTEN, KCNQ4, and BRCA1), we find that AlphaMissense is consistently one of the top five algorithms based on correlation with functional impact and is the best-correlated algorithm for two genes. We conclude that AlphaMissense represents the current best-in-class predictor by this metric; however, the improvement over other algorithms is modest. We note that multiple missense predictors, including AlphaMissense, appear to overcall variants as pathogenic despite minimal functional impact and that substantially more high-quality training data, including consistently analyzed patient cohorts and MAVE analyses, are required to improve accuracy.

genetics↗