Neurofilament-lysosomal genetic intersections in the cortical network of stuttering

Abstract

The neurobiological underpinnings of stuttering, a speech disorder characterized by disrupted speech fluency, remain unclear. While recent developments in the field have afforded researchers the ability to pinpoint several genetic profiles associated with stuttering, how these specific genetic backgrounds impact neuronal circuits and how they generate or facilitate the emergence of stuttered speech remains unknown. In this study, we identified the large-scale cortical network that characterizes stuttering using functional connectivity MRI and graph theory. We performed a spatial similarity analysis that examines whether the topology of the stuttering cortical network intersects with genetic expression levels of previously reported genes for stuttering from the protein-coding transcriptome data of the Allen Human Brain Atlas. We found that GNPTG - a gene involved in the mannose-6-phosphate lysosomal targeting pathways - was significantly co-localized with the stuttering cortical network. An enrichment analysis demonstrated that the genes identified with the stuttering cortical network shared a significantly overrepresented biological functionality of Neurofilament Cytoskeleton Organization (NEFH, NEFL and INA). The relationship between lysosomal pathways, cytoskeleton organization, and stuttering, was investigated by comparing the genetic interactome between GNPTG and the neurofilament genes implicated in the current study. We found that genes of the interactome network, including CDK5, SNCA, and ACTB, act as functional links between lysosomal and neurofilament genes. These findings support the notion that stuttering is due to a lysosomal dysfunction, which has deleterious effects on the neurofilament organization of the speech neuronal circuits. They help to elucidate the intriguing, unsolved link between lysosomal mutations and the presence of stuttering.

Keywords: Cortical network; Genetics; Lysosomal; Neurofilament; Stuttering.

Figure 1.

Diagram of Graph Theory Metrics and Connectomics-Genetics Similarity Approach. The stuttering network was characterized using 1) regions of interest identified from a meta-analysis of fMRI studies, 2) whole-brain low-frequency BOLD fluctuations and 3) two graph theory strategies (I). In graph-theory strategy 1, we calculated the functional connectivity patterns of brain voxels (light blue nodes in I) that connect to stuttering-related regions of interest (dark nodes or targets in I). In graph-theory strategy 2, we calculated the functional connectivity patterns of brain voxels (orange and red nodes, interconnectors in I) that reach a percentage of stuttering-related regions of interest simultaneously. Genetic expression data of stuttering- and language-related genes (II) were analyzed and compared with the stuttering network using a Euclidean distance approach (III).

Figure 2.

Cortical Network Underlying Stuttering. Regions of interest obtained from a meta-analysis of fMRI and PET activation studies in stuttering (I). Stuttering Network characterization based on functional connectivity of normally fluent controls (NFC) in adults (left) and children (right) samples (from graph theory Strategy 1 and 2 (20% to 70% visualization); II) and group contrast between children who stutter (CWS) and NFC (III). Color scale in I represents the z-score transformation of the weighted degree centrality score (minimum = 0SD and maximum = 2SD). Color scale in II represents the whole spectrum of permutation-based corrected t-test values. R: right. L: left.

Figure 3.

Stuttering Network versus Other Language-Related Networks. Cortical templates of the auditory-motor integration network, Wernicke’s network, Broca’s F3 opercularis, Broca’s F3 triangularis, and Broca’s F3 orbitalis, are represented in I (adapted from Sepulcre, 2013). Overlap between these language-related networks and the stuttering network in flat projections (II) and bar graph (III). Color scale in II represents the z-score transformation of the weighted degree centrality score (minimum = 0SD and maximum = 2SD).

Figure 4.

Stuttering Network Topology and Genetic Expression Levels of the Human Cortex. Spatial similarity (or co-expression pattern) between genes previously described as stuttering and language-related (matrix and hierarchical clustering of Euclidean distances; I). Distribution of all similarity scores between the stuttering network and the entire transcriptome data from the Allen Human Brain Atlas (histogram of Euclidean distances; II). Comparative topology of cortical projections (regular and flat) between the stuttering network (left) and gene expression levels of GNPTG (right) in Desikan-Killiany atlas space (III). Gene Ontology Overrepresentation analysis of genes displaying statistically significant similarity scores with the stuttering network (red horizontal in histogram in II and III). Color scale in III represents the z-score transformation of the weighted degree centrality score (minimum = −2SD and maximum = 2SD) and the AHBA score of GNPTG transcripts (minimum = 2% and maximum = 98%).

Figure 5.

Genetic Interactome Analysis Between GNPTG and Neurofilament Genes. Genetic network (non-brain tissue based) and betweenness centrality of the interactions between GNPTG and neurofilament genes (NEFH, NEFL and INA).