Understanding the biology of language impairment through whole genome sequencing
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PROVIDER: PRJNA671391 | ENA |
REPOSITORIES: ENA
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Project description:Understanding the biology of language impairment through whole genome sequencing
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Project description:Language is a unique human capability with limited molecular understanding due to the lack of animal models and technical constraints. Transcriptomics analysis offers a comprehensive view of gene expression in specific tissues, aiding the understanding of their functions. However, such patterns have been underexplored in language-related regions of the human brain. This study conducts a comprehensive transcriptomic analysis of 125 samples from 13 language-related Brodmann areas (BAs) in both hemispheres of five human postmortem brains. The expression landscape of human language-related regions is mapped, revealing higher expression in the right hemisphere, notably BA45 (Broca’s area) and BA3/1/2 (ventral sensory-motor cortex). Integrative analysis of differentially expressed genes and language-relevant genetic discoveries provides insights into the rs62060948 locus. The findings suggest that the rs62060948-MYC-WNT3 axis plays a crucial role in language function in BA44 of the right hemisphere. Behavior tests in mice show that Wnt3 knockdown in the right auditory cortex leads to abnormal behaviors, confirming that imbalanced Wnt3 expression impacts language function. Pathway analysis indicates that Wnt3 maintains nervous system development through neuron ensheathment. This study enhances understanding of the molecular mechanisms underlying human language function and identifies potential targets for language disorder therapies.
2025-01-29 | GSE287961 | GEO
Project description:Analysis of transcript abundance estimates as a function of demographic, psychometric, and language use features.
2017-12-05 | GSE87656 | GEO
Project description:New functional signatures for understanding melanoma biology from tumor cell lineage-specific analysis
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Project description:Developmental language disorder (DLD), previously known as specific language impairment, is a neurodevelopmental disorder. It affects approximately 7% of school-age children. The affected children fail to develop normal speech and language skills. This is a major public health concern as it adversely impacts the communication, academic, and social skills of the affected individual. The human brain development is a complex process that involves the accurate orchestration of the expression of multiple genes. Precise temporal and spatial regulation of gene expression is essential for proper brain development. Epigenetic factors such as DNA methylation can modulate gene expression without altering the DNA sequence. They are, therefore, considered as key regulators of the expression of genes involved in neurodevelopment. In this study, we examined any altered DNA methylation between children affected with DLD and healthy control subjects. We looked into genome-wide methylation differences between the DLD and control groups using Infinium HumanMethylation850 (EPICarray). Twelve children with DLD and 12 healthy controls were recruited for the study. Five milliliters of peripheral blood samples were collected from the study subjects in EDTA vials. Genomic DNA (gDNA) was extracted from the blood samples using the standard salting out protocol. Five micrograms of each DNA at 50 ng/µl concentration were used for genome-wide methylation analysis. The gDNA samples were bisulfite-treated with EpiTect Bisulfite Kit. Further to this, the DNA samples were subjected to whole genome amplification and enzymatic fragmentation. Human Infinium Methylation EPIC BeadChip (Illumina), which covers more than 850,000 genome-wide methylation sites, was used for genome-wide methylation analysis. The DNA methylation profiles of each sample were visualized at the single-CpG level and for the genomic regions of interest.
2024-01-10 | E-MTAB-13583 | biostudies-arrayexpress
Project description:Half of all amyotrophic lateral sclerosis (ALS) patients demonstrate a spectrum of cognitive and behavioral changes over the course of the disease, but the mechanisms underlying this heterogeneity remain unclear. We assemble a high-resolution cellular map of prefrontal cortex regions of the ALS brain by integrating spatial and single-nucleus transcriptomic profiles of a cognitively stratified ALS patient cohort that includes non-neuropathological controls. We find cellular programs characteristic of ALS, including a frequent gliotic response. We also find that executive and language cognitive impairments differ from ALS without cognitive impairment, and from each other, in the extent and pattern of neuronal dysregulation and neuron-glial interactions across different brain regions. These findings reveal a relationship between cognitive phenotype and prefrontal cortex dysfunction in ALS.
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Project description:Genetic admixture and language shift in the medieval Volga-Oka interfluve
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Project description:While the genome is composed of individual nucleotides, functional elements such as cis-regulatory elements and structural interactions are formed from sets of interdependent nucleotides. In principle, these dependencies are reflected in coevolutionary relationships. However, classical comparative genomics approaches struggle to detect these dependencies beyond alignable highly conserved sequences such as within coding regions. DNA language models (LMs), which are trained by predicting nucleotides given their sequence context, have recently been proposed as foundational models for sequence-based prediction problems. DNA LMs implicitly capture functional elements from genomic sequences alone. However, which dependencies DNA LMs learn and whether they reflect known or even novel biology remains an open question. Here we introduce nucleotide dependency maps to systematically study nucleotide dependencies captured by DNA LMs in a purely unsupervised setup. We compute these maps genome-wide and show that they reveal and clearly delineate known functional genomic features such as transcription factor binding motifs, functional interactions between splice sites, RNA tertiary structures, and coding sequences. This allowed to uncover novel experimentally validated RNA structures. We furthermore investigate dependency maps from in silico manipulated sequences, revealing the ability of DNA LMs to capture operations such as copying and reverse complementarity without memorization. Lastly, we compare dependency maps from openly available DNA LMs, showcasing the drawbacks and advantages of different models. We find stark differences in the ability of models to accurately learn conserved but infrequent features. Altogether, by leveraging the flexibility of DNA language models, nucleotide dependency mapping emerges as a general methodology to discover and study functional interactions in genomes.
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