3rd Year Microbiology Major
Assistant Professor
Microbiology Department
Enteric pathogens regulate the expression of their virulence and proliferation genes through the recognition and exploitation of environmental metabolites. Pathogens utilize such compounds to increase or decrease transcription of virulence genes, allowing them to choose between energetically expensive virulence programs or gut proliferation based on surrounding conditions. This practice is crucial for pathogen success as it prevents spending valuable resources on invasion genes before optimal population levels and growth conditions are met, while minimizing damage from a host immune response.
The bacterial pathogen Shigella flexneri is one example of such masterful use of metabolites. We hypothesized that S. flexneri can utilize intestinal fatty acids as signals to regulate a critical step in the infection strategy: cell-to-cell spread. This movement between colon epithelial cells is mediated by icsA, activated by master transcriptional regulator VirF, which has itself been shown to be repressed by fatty acids. Here we show that dietary and microbially sourced fatty acids can be exploited by S. flexneri to reduce the expression of icsA and hence cell-to-cell spread.
Pathogenic bacteria are microbes that have virulence genes. These genes give them the ability to make you sick! Virulence genes are also very metabolically expensive-they take a lot of resources to use. So, pathogens need to make a decision: do they use their energy resources to keep dividing and growing, or to cause virulence?
And how does a little microbe "know" when virulence should be on or off? It can look in its surroundings for signalling molecules-like fatty acids-that can tell it something about location or timing!
Shigella flexneri is a pathogenic bacteria that infects your colon after you ingest it. This microbe causes bacterial dysentery, a severe diarrheal disease.
S. flexneri's virulence genes lead to abilities like secretion of proteins, spreading between cells, and inflammation-causing mechanisms. When the bacteria decide its time for virulence, it turns on VirF, a protein called a master activator. VirF turns on all other virulence genes.
When S. flexneri invades your colon, it enters one of the epithelial cells and then travels between them before disupting the layer in a flood of inflammation! The movement between the cells is crucial for virulence, and the gene responsible for cell-to-cell spread is icsA.
So, if pathogenic bacteria need to use environmental compounds to decide when to turn virulence on/off, then Shigella flexneri must be using compounds in the colon (fatty acids) to control the expression of icsA, a virulence gene controlling cell-to-cell spread!
How does Shigella flexneri use intestinal fatty acids in its surroundings to control virulence, specifically cell-to-cell spread through icsA?
First, I needed to be able to "see" how much icsA was being expressed in my bacteria. I built a plasmid-a small circular DNA segment-where icsA expression is connected to luminescence. So, when icsA is "on", light is produced!
Next, I put my bacteria with this icsA-luminescence plasmid in a plate reader machine that can read light. I put different concentrations of 3 major colonic fatty acids in these plates-oleic, palmitic, and cis-2-hexadecenoic acid. The plate reader measured how much icsA was "on" or "off" when these fatty acids were present!
Lastly, I wanted to see why icsA was being repressed by fatty acids. Is it because fatty acids bind to VirF and block it from turning icsA on? I used artificial intelligence websites to model how VirF and fatty acids probably bind!
Based on my plate reader results, I saw repression (less expression) of icsA when all three fatty acid types were present!
I also saw different levels of repression, like up to a 3 fold reduction in oleic acid, but only up to 1.5 fold in palmitic acid. Why would different fatty acids have different repression levels? Becuase they are different molecules! Different molecules bind differently to proteins like VirF, so they can have greater or less impact on icsA expression.
This model shows how VirF (grey) is predicted to bind to oleic acid (green) in the binding area (blue).
This model shows how VirF (grey) is predicted to bind to palmitic acid (green) in the binding area (blue).
This model shows how VirF (grey) is predicted to bind to cis-2-hexadecenoic acid (green) in the binding area (blue).
So...VirF IS predicted to bind to all three fatty acids. This might explain why icsA is being repressed when these fatty acids are present. The fatty acids in the colon "de-activate" VirF, so it cannot turn icsA on. VirF blocked, no icsA, no cell-to-cell spread!
icsA gene expression is significantly reduced by colonic fatty acids
Fatty acids are predicted to bind to icsA's activator VirF
Shigella flexneri is using environmental metabolites to control cell-to-cell spread!
In the future...
Confirm VirF- fatty acid binding with "human intelligence" instead of "artificial intelligence" aka binding assays
Test if S. flexneri's ability to spread between real colon cells is reduced if fatty acids are present
Test more colonic fatty acids! Next up: palmitoleic acid.
The GutHub Lab (learn more here: https://chowdhurylab.net/)
Department of MBI at Miami University
Career and Self Development: I was able to present at the Ohio Branch of the American Society for Microbiology (OBASM) Conference using this research, allowing me to network and gain valuable skills in my field.
Communication: through this research, I gained skills of communication by making and presenting posters, drafting papers, and applying for grants and scholarships.
Teamwork: this project was only possible through collaboration between multiple lab members. I was able to gain skills of teamwork by supporting and being supported by my labmates.
Leadership: through this research we were able to have opportunities to make decisions for the entire lab group, teach methods to each other, offer ideas, and act as leaders in our group.
All figures made in BioRender and graphs in GraphPad