Biology subjects

O Broin, P.

Publications and source records attributed to O Broin, P..

6 recordsLinked to original sources

bioRxiv2026

Neonatal social communication and single genes predict the variability of post-pubertal social behavior in a mouse model of paternal 15q11-13 duplication

Mental illnesses associated with high-risk copy number variations (CNVs) are characterized by incomplete penetrance and variable severity, with their underlying mechanisms remaining inadequately understood. We hypothesized that such phenotypic variability is evident from the neonatal stage and is, at least in part, attributable to individual differences in the expression levels of CNV-encoded genes in the brain. We conducted an analysis of the quantitative and functional structure of neonatal social communication, assessed post-pubertal social interaction, and evaluated the brain expression levels of genes within the same cohort of a mouse model of paternal human 15q11-13 duplication, a high-risk factor variably associated with neurodevelopmental disorders. Subsequently, computational methods were utilized to identify predictive variables for the variability of post-pubertal social interaction. Mice harboring the 15q11-13 duplication exhibited distinctive call sequences characterized by diverse connections, which lacked the incentive value necessary for effective social communication with mother mice. The neonatal call sequences and the expression levels of Magel2, along with, to a lesser extent, Herc2 and Ndn, in the prefrontal cortex of the 15q11-13 duplication model were predictive of post-pubertal social interaction. Our findings demonstrate that variability in post-pubertal social interaction--a dimensional characteristic of neurodevelopmental disorders--can be predicted by the variability of neonatal social communication and is influenced by the expression levels of specific CNV-encoded genes in the prefrontal cortex. This computational approach has the potential to predict the developmental trajectories of various dimensions of mental illness among CNV carriers in humans and to identify CNV-encoded driver genes in preclinical models, thereby providing potential mechanistic bases for the development of gene-based therapeutic strategies.

neuroscience↗

bioRxiv2022

Comparing the XGBoost machine learning algorithm to polygenic scoring for the prediction of intelligence based on genotype data

A polygenic score (PGS) is a linear combination of effects from a GWAS that represents and can be used to predict genetic predisposition to a particular phenotype. A key limitation of the PGS method is that it assumes additive and independent SNP effects, when it is known that epistasis (gene interactions) can contribute to complex traits. Machine learning methods can potentially overcome this limitation by virtue of their ability to capture nonlinear interactions in high dimensional data. Intelligence is a complex trait for which PGS prediction currently explains up to 5.2% of the variance, a relatively small proportion of the heritability estimate of 50% obtained from twin studies. Here, we use gradient boosting, a machine learning technique based on an ensemble of weak prediction models, to predict intelligence from genotype data. We found that while gradient boosting did not outperform the PGS method in predicting intelligence based on SNP data, it was capable of achieving similar predictive performance with less than a quarter of the SNPs with the top SNPs identified as being important for predictive performance being biologically meaningful. These results indicate that ML methods may be useful in interpreting the biological meaning underpinning SNP-phenotype associations due to the smaller number of SNPs required in the ML model as opposed to the standard PGS method based on GWAS.

bioinformatics↗

bioRxiv2022

Single-cell characterisation of the hematopoietic bone marrow interactome in health and disease

1The bone marrow (BM) is a complex microenvironment and the primary site of hematopoiesis, coordinating the production of billions of blood cells every day. Despite the essential role of the hematopoietic niche in maintaining hemostasis and its relevance to hematopoietic diseases, many aspects of this environment remain poorly characterised due to experimental hurdles. Here we present a high-resolution characterisation of the niche in health and acute myeloid leukemia (AML) by establishing a comprehensive single-cell gene expression database of nearly 340,000 BM constituent cells encompassing all disease stages (healthy BM, AML at diagnosis, remission and relapse). We characterised the cell type composition of the BM and found that the proportions of both myeloid and lymphoid lineage cell types are significantly altered in AML. We also determined broadscale dysregulation of gene expression in almost all BM cell types upon establishment of AML, indicating that the entire niche is disrupted by the disease. Given the importance of interactions between hematopoietic cells and their microenvironment in regulating their function and properties, we determined all possible ligand-receptor interactions between hematopoietic stem and progenitor cells (HSPC) and every other BM constituent cell type. This analysis revealed a remarkable expansion of HSPC interactions in AML involving multiple BM constituent cells that can drive dysregulated HSPC-cell adhesion, immunosuppression and enhanced cytokine signalling. In particular, we found that interactions involving TGFB1 become widespread in AML and present evidence that these interactions can drive AML cell quiescence in vitro, thus highlighting TGFB1 signalling as a potential target for increasing drug sensitivity and preventing relapse. Our results shed light on potential mechanisms of enhanced competitiveness of AML HSPCs and an overall skewed microenvironment that fosters AML growth.

cancer biology↗

bioRxiv2021

Transcriptional regulation of neonatal neural stem cells is a determinant of social behavior

Rare gene variants confer a high level of penetrance to neurodevelopmental disorders, but their developmental origin and cellular substrates remain poorly understood. To address this limitation, we explored the role of TBX1, a gene encoded in a rare copy number variant, in cell and mouse models. Here, we report that neonatal Tbx1 deficiency contributes to defective peripubertal social behavior and impairs the proliferation of neonatal neural stem/progenitor cells. Moreover, TBX1 transcriptionally regulates genes linked to post-embryonic neurogenesis and neurodevelopmental disorders associated with other rare gene variants. Our data indicate a precise time window and cell type through which the social dimension is altered by a gene encoded in a rare CNV and provide a potential common mechanistic basis for a group of neurodevelopmental disorders. One-Sentence SummaryTbx1, a gene affecting neonatal stem cell proliferation, influences peripubertal social behavior.

neuroscience↗

bioRxiv2021

Identification of 13 independent genetic loci associated with cognitive resilience in healthy aging in 330,097 individuals in the UK Biobank.

Cognitive resilience is the ability to withstand the negative effects of stress on cognitive functioning and is important for maintaining quality of life while aging. UK Biobank (UKB) does not have direct measurements of the same cognitive phenotype at distal timepoints. Therefore, we used number of education years (EY) as a proxy phenotype for past cognitive performance. Current cognitive performance was determined based on processing speed. This approach captured an average time span of 40 years between past and current cognitive performance in 330,097 individuals. A confounding factor was that EY is highly polygenic and masked the genetics of resilience. To overcome this, we employed Genomics Structural Equation Modelling (GenomicSEM) to perform a GWAS-by-subtraction using two GWAS, one GWAS of EY and resilience and a second GWAS of EY but not resilience. Subtracting one from the other generated a GWAS of Resilience. Replication of this approach was shown using independent discovery and replication samples within UKB and the full UKB GWAS results show significant genetic correlation with a GWAS of cognitive change in the independent Health and Retirement Study (N=9,526; P=1.5x10-3). We found 13 independent genetic loci for Resilience. Functional analyses showed enrichment in several brain regions and involvement of specific cell types, including GABAergic neurons (P=6.59x10-8) and glutamatergic neurons (P=6.98x10-6) in the cortex. Gene-set analyses implicated the biological process "neuron differentiation" (P=9.7x10-7) and the cellular component "synaptic part" (P=2.14x10-6). Mendelian randomization analysis showed a causative effect of white matter volume on cognitive resilience. These results enhance neurobiological understanding of resilience.

genomics↗

bioRxiv2019

Genes influenced by MEF2C contribute to neurodevelopmental disease via gene expression changes that affect multiple types of cortical excitatory neurons

Myocyte enhancer factor 2 C (MEF2C) is an important transcription factor during neurodevelopment. Mutation or deletion of MEF2C causes intellectual disability (ID) and common variants within MEF2C are associated with cognitive function and schizophrenia risk. We investigated if genes influenced by MEF2C during neurodevelopment are enriched for genes associated with neurodevelopmental phenotypes, and if this can be leveraged to identify biological mechanisms and individual brain cell types affected. We used a set of 1,052 genes that were differentially expressed in the adult mouse brain following early embryonic deletion of Mef2c in excitatory cortical neurons. Using GWAS data, we found these differentially expressed genes (DEGs) to be enriched for genes associated with schizophrenia, intelligence and educational attainment but not autism spectrum disorder (ASD). Using sequencing data from trios studies, we found these DEGs to be enriched for genes containing de novo mutations reported in ASD and ID, but not schizophrenia. Using single cell RNA-seq data, we identified that a number of different excitatory glutamatergic neurons in the cortex were enriched for these DEGs including deep layer pyramidal cells and cells in the retrosplenial cortex, entorhinal cortex and subiculum. These data indicate that genes influenced by MEF2C during neurodevelopment contribute to cognitive function and risk of neurodevelopmental disorders. Within excitatory neurons, common SNPs in these genes contribute to cognition and SZ risk via an effect on synaptic function based on gene ontology analysis. In contrast, rare mutations contribute to earlier onset ASD and ID via an effect on cell morphogenesis. Author SummarySchizophrenia is a complex disorder caused by many genes. Current drugs for schizophrenia are only partially effective and do not treat cognitive deficits, which are key factors for explaining disability. Here we take an individual gene identified in genetic studies of schizophrenia and cognition called MEF2C, which on its own is a very important regulator of brain development. We use data from a mouse study where MEF2C has been stopped from functioning or knocked out during brain development. The effect of that knock out has been measured when the mice reach adulthood, in the form of a set of differentially expressed genes (DEGs) from the somatosensory cortex. We found that this set of DEGs contains more genes than expected by chance that are associated with schizophrenia and cognition or contain rare new (de novo) mutations reported in autism and intellectual disability. Using gene expression data from single brain cells, we identified that a number of specific excitatory glutamatergic neurons in the cortex were enriched for these DEGs. This study provides evidence that the molecular mechanisms that underpin schizophrenia and cognitive function include disruption of cell types influenced by MEF2C.

genomics↗