Biology subjects

Ghosal, S.

Publications and source records attributed to Ghosal, S..

3 recordsLinked to original sources

bioRxiv2022

Mitofusin-2 in nucleus accumbens D2-MSNs regulates social dominance and neuronal function

The nucleus accumbens (NAc) is a brain hub regulating motivated behaviors, including social competitiveness. Mitochondrial function in the NAc is critically implicated in the association between anxiety and social competitiveness, and the mitochondrial fusion protein mitofusin 2 (Mfn2) in NAc neurons has been shown to regulate anxiety-related behaviors. However, it remains unexplored whether accumbal Mfn2 levels also affect social behavior and whether Mfn2 actions in the emotional and social domain are driven by distinct cell types. Here, we found that subordinate-prone highly anxious rats show reduced accumbal Mfn2 levels and that Mfn2 overexpression promotes dominant behavior. In mice, selective Mfn2 downregulation in NAc dopamine D2 receptor-expressing medium spiny neurons (D2-MSNs) induced social subordination, accompanied by reduced mitochondrial function and decreased neuronal excitability. Instead, D1-MSN-targeted Mfn2 downregulation affected competitive ability only transiently mainly by increases in anxiety-like behaviors. Our results assign dissociable cell-type specific roles to Mfn2 in the NAc in modulating social dominance and anxiety.

neuroscience↗

bioRxiv2022

Combining phenotypic and genomic data to improve prediction of binary traits

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here main traits) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or phenotypes) that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any phenotypic variables. We develop a new statistical technique that ensures appropriate representation from both the secondary traits and the phenotypic variables for optimal prediction. When two data types (markers and secondary traits) are available, we achieve improved prediction of a binary trait by two steps that are designed to ensure that a significant intrinsic effect of a phenotype is incorporated in the relation before accounting for extra effects of genotypes. First, we sparsely regress the secondary traits on the markers and replace the secondary traits by their residuals to obtain the effects of phenotypic variables as adjusted by the genotypic variables. Then, we develop a sparse logistic classifier using the markers and residuals so that the adjusted phenotypes may be selected first to avoid being overwhelmed by the genotypes due to their numerical advantage. This classifier uses forward selection aided by a penalty term and can be computed effectively by a technique called the one-pass method. It compares favorably with other classifiers on simulated and real data.

genomics↗

bioRxiv2021

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Phenotypes of Disease

AO_SCPLOWBSTRACTC_SCPLOWWe propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.

bioinformatics↗