USC (US) — An enzyme’s random behavior is crucial to antibody diversity and how the immune system works to keep the body healthy.
“Why don’t you die when I sneeze? It’s because you have a powerful immune system,” says Myron Goodman, professor of biological sciences and chemistry at the University of Southern California. “And the way to get a decent immune system is for your body to have a way to respond to insults it has never seen before.”
The research is reported in the Journal of Biological Chemistry.
Having variation in the types of antibodies produced by your body is what gives it a fighting chance, Goodman says. Antibodies protect against invasion by antigens such as bacteria or viruses by locating them in the body and neutralizing them. To do that, antibodies must bind to antigens.
The more variation in the types of antibodies produced by the body, the more likely they will be able to bind to and fight off antigens, which come in many forms.
To create antibody diversity, mutations must occur in the variable region of immunoglobulin genes, the region where antibodies bind to invaders. Generating those mutations has to be a really random process, Goodman says. That’s where the enzyme, called activation-induced deoxycytidine deaminase (AID) steps in.
Goodman and colleagues monitored the actions of AID as it scanned single-stranded DNA or transcribed double-stranded DNA. The enzyme essentially moves back and forth along the DNA strand and sporadically converts cytosine to uracil triggering a mutation in tri-nucleotide motifs—sequences comprising three bases—found along the DNA.
Unlike most enzymes that are exquisitely efficient in targeting favored motifs, AID is the polar opposite, initiating chemical reactions in favored motifs inefficiently—only about 3 percent of the time. By mutating the motifs so haphazardly, it is able to produce antibody diversity.
To identify and describe AID’s complex process during scanning, the researchers used a genetic assay to measure the distribution of AID-induced mutations on individual DNA molecules and then analyzed the mutational data computationally using a random walk model.
By combining the genetic and computational analyses, they were able to calculate the distribution of mutations that occurred with a remarkable fit to their experimental data.
The fit entailed matching theory to experiment for the patterns of closely spaced mutations and separately for the distances between mutated and non-mutated target motifs.
The study sheds light on a little-studied group of enzymes like AID that scan single-stranded DNA, Goodman says.
“This is the first really clear picture of what AID is doing during the scanning process.”
More news from USC: http://uscnews.usc.edu/