Centrosome Regulation in Development and Dysregulation in Disease

National Heart, Lung, And Blood Institute

Investigators

Linked publications, trials & patents

Abstract

Understanding how events at the molecular and cellular scales contribute to tissue form and function is key to uncovering mechanisms driving animal development, physiology and disease. Elucidating these mechanisms has been enhanced through the study of model organisms and the use of sophisticated genetic, biochemical and imaging tools. In the past fiscal year, we have continued our longtime work aimed at understanding centrosome function in difference context. Our main focus now is investigating the linkage between the centriole and nucleus as haploid cells develop into sperm. Proper connection between the sperm head and tail is critical for sperm motility and fertilization. Head-tail linkage is mediated by the Head-Tail Coupling Apparatus (HTCA), which secures the axoneme (tail) to the nucleus (head). However, the molecular architecture of the HTCA is poorly understood. Here, we use Drosophila to investigate formation and remodeling of the HTCA throughout spermiogenesis by visualizing key components of this complex. Using structured illumination microscopy, we demonstrate that key HTCA proteins Spag4 and Yuri form a 'Centriole Cap' that surrounds the centriole (or Basal Body) as it invaginates into the surface of the nucleus. As development progresses, the centriole is laterally displaced to the side of the nucleus while the HTCA expands under the nucleus, forming what we term the 'Nuclear Shelf.' We next show that the proximal centriole-like (PCL) structure is positioned under the Nuclear Shelf, functioning as a critical stabilizer of centriole-nuclear attachment. Together, our data indicate that the HTCA is a complex, multi-point attachment site that simultaneously engages the PCL, the centriole, and the nucleus to ensure proper head-tail connection during late-stage spermiogenesis. Following from this work, we aimed to uncover genomic regions, and then specific genes, that are critical for HTCA formation. To begin this work, we undertook a two part screen using the well-known genetic tool in Drosophila – a deficiency (Df) kit. For this screen, we utilized a sensitized genetic background that overexpresses the pericentriolar material regulatory protein Pericentrin-Like Protein (PLP). We had previously shown that PLP overexpression (PLPOE) disrupts the head-tail connection in some spermatids, but not to a degree sufficient to reduce fertility. In the first step of the screen we tested for Dfs that in combination with PLPOE cause a reduction in fertility. We ultimately identified 11 regions of the genome that showed an enhanced fertility defect when combined with PLP overexpression. In the second step of the screen we tested these Dfs for their ability to enhance the HTCA defect caused by PLPOE, finding six. We then tested smaller Dfs to narrow the region of the genome that contained these enhancers. To further analyze the regions of the genome removed by these Dfs, we examined the expression patterns of the genes within these Dfs in publicly available datasets of RNAseq of Drosophila tissues and snRNAseq of Drosophila testes. In total, our analysis suggests that some of these Dfs may contain a single gene that might influence HTCA formation and / or fertility, while others appear to be regions of the genome especially rich in testis-expressed genes that might affect the HTCA because of complex, multi-gene interactions. We are currently following up on many of these hit, specifically ones that we have been able to localizes to the HTCA using a follow-up GFP localization screen of candidates. We will report on our finding the next annual report. Finally, our lab has been working on a new initiative to expand our capabilities and begin modeling human diseases on two fronts. First, we have been using our cell and developmental approaches to investigate a centrosome dysfunctions linked to developmental disorders affecting brain and body size, such as primordial dwarfism and microcephaly. In this study, we investigate a mutation in Pericentrin, a centrosome-associated protein, found in families with Microcephalic Osteodysplastic Primordial Dwarfism type II (MOPD II). Unlike typical pathogenic mutations that cause severe protein truncation, this mutation involves a single amino acid deletion in the protein’s only conserved functional domain, providing a unique opportunity to explore Pericentrin function in MOPD II. Using Drosophila as a model, we examined the effects of Pericentrin-like protein (PLP) carrying an orthologous deletion (plpΔR). PLP is essential for pericentriolar material (PCM) recruitment, spermatogenesis, and sensory cilia formation. Our results show that plpΔR reduces PCM levels by ~25%, impairing centrosome organization in both symmetric and asymmetric divisions. We also find that plpΔR increases apoptosis, reduces mitotic index, and accelerates mitotic divisions in both epithelial and brain tissues. In the brain, these changes result in a reduced neural stem cell pool. Behavioral assays revealed defects in gravitaxis and mechanosensation, suggesting impaired sensory cilia function. Yeast two-hybrid and Co-IP experiments show that plpΔR disrupts dimerization and the interaction with Asterless (Asl), another centrosome protein. Overall, these findings suggest that plpΔR is a hypomorphic allele with tissue-specific effects linked to MOPD II's clinical manifestations. Our work offers new insights into the failed cellular mechanisms underlying MOPD II, potentially linking this human disease to the loss of a single protein-protein interaction. This work had led to collaborations with the NIH undiagnosed disease program where we are not identifying patient mutations that are linked to centrosome genes. Future work will focus on these rare diseases, establishing a fly model for each. The second major undertaking from the lab is to establish a high-throughput screening pipeline for Drosophila models of human disease that will allow for rapid identification of disease phenotype suppressors and thus contribute to development of effective treatment options. Historically, Drosophila researchers relied heavily on direct observation under a microscope to identify and study mutant phenotypes. Although direct observation does not lead to deep mechanistic insight, its major advantage is lack of experimental manipulation and thus minimal preparation time, both desirable qualities for screens. However, the time required for observation is the main bottleneck of this direct approach. We are aiming to overcome this bottleneck using automation and artificial intelligence, thus facilitating high-throughput screens using direct observation with minimal time cost. To this end, we developed a high-throughput imaging platform for recording high-resolution movies of fruit flies, and are developing a corresponding data analysis pipeline that uses artificial intelligence to classify movies at different levels. Our imaging platform has a modular design with multiple recording units consisting of a Raspberry Pi 5 controlling an IMX477 camera, which can be scaled in number depending on the user’s need. Our initial goal is to analyze fly behavior. The first step in data pre-processing is to apply a custom DeepLabCut foundation model that applies 30 keypoints to each frame of the movie. Keypoints data are pre-processed using custom scripts to define a set of behavior and orientation-based labels and generate initial measurements and engineered features. Pre-processed keypoints and features are then passed as input into a transformers-based neural network, which can be trained to classify movies at different levels of granularity. We are currently working on improving model performance and implementing a morphology-based pipeline. As part of this initial phase of the project, we have generated several mutant fly strains modeling diseases from the NHLBI Clinical Center. We have recorded and are analyzing data from several Drosophila mutants, including mutants with known behavioral defects. We trained an initial transformers-based model that distinguishes between mutant and control animals with high accuracy. Our preliminary work lays the foundation for high-throughput screens to identify clinically-relevant pathways involved in human rare disease phenotypes.