Week 2: FAK and Schizophrenia

This week we took a closer look at a paper that investigated cell adhesion, cell motility, and focal adhesion dynamics in Schizophrenia patients.

These migration functions are regulated by focal adhesion kinase (FAK) proteins. The focal adhesion kinase signaling pathway involves the expression of integrin genes; integrins are proteins that detect cell adhesion as well as attach the cell to the extracellular matrix during cell migration. A previous study showed that the expression of two integrin genes (ITGA8, ITGA3) was altered in schizophrenia-derived cells.

The study was conducted on olfactory neurosphere-derived cells (accessible via biopsy of the olfactory mucosa) from 9 healthy male subjects and 9 male schizophrenia patients using two different assays. The assays involved seeding the cells on fibronectin-coated plates or chambers, allowing them to attach for 4 hours, washing away non-adherent cells, and allowing remaining cells to migrate for different periods of time. These experiments were repeated with the presence of FAK phosphorylation inhibitors, and then again while blocking antibodies to two different types of integrins. Several pieces of data were analyzed, including levels of pFAK, migration distance, speed, and size and number of focal adhesions present in the cells.

The results of this study demonstrated several important disparities that we are paying close attention to. While there was no difference in levels of FAK between patients and control subjects, patients had significantly lower levels of phosphorylated FAK (pFAK). In addition, patient cells had fewer adhesions, were less adherent, and were more motile than control cells, with a higher percentage of patient cells migrating further and with greater speed.  When pFAK was inhibited, and antibodies to two different types of integrins were blocked (three separate experiments), patient cell motility was reduced to control levels but control levels were not changed. This last result led us to conclude that the phosphorylation of FAK does not work like an on/off switch; rather, phosphorylation of FAK alters its behavior.

This paper gave us lots of food for thought; it provided us with a starting place to form our list of schizophrenia genes to investigate. Next week we will be investigating cell motility further by finding cell motility gene databases and papers studying the link between cell motility and schizophrenia.

 

 

 

Week 1: A Little Context

Week 1! We’re starting off the year, and our research, with some literature review to provide ourselves with a little context. Our proposed research plan mainly draws on two papers that laid out the methodological groundwork for us.

The first paper provides pertinent results and insights into the connection between cell motility and schizophrenia. The researchers concluded that cells in patients with schizophrenia are less adhesive and more motile than the cells of healthy control subjects, which was shown to improve with the inhibition of the focal adhesion kinase (FAK) protein. The results of this paper showed that there is a correlation between the altered motility of patient cells and dysregulated gene expression in the FAK signaling pathway within these cells. This paper provided us with the informational basis necessary to choose what kinds of genes we will shine our focus on – schizophrenia and migration association genes.

The second paper essentially laid out the framework for our methodology. In this paper, the researchers used a machine-learning algorithm to predict genes associated with autism spectrum disorder (ASD). They then validated these predicted genes experimentally using an independent-case sequencing study and were further able to demonstrate that this large set of ASD genes played roles in key pathways and brain development.

Pulling from both these papers, we plan to develop a network diffusion algorithm to identify candidate schizophrenia and migration associated genes. We are going to computationally build a list of predicted genes and (hopefully) validate them experimentally. However, before we do that, we need to delve into the nitty gritty of how the autism paper’s machine-learning algorithm was created.

In the paper, it outlines how the approach was based upon a human brain-specific gene functional-interaction network nicknamed GIANT (Genome-scale Integrated Analysis of gene Networks in Tissues). We began with a few initial questions: How is a functional-interaction standard set up? How was GIANT constructed? These questions will no doubt open up a whole new corridor of doors to explore; over the next couple of weeks, our goal is to investigate these questions through literature review and learn more about how we can utilize these tools in the coming year.