Everyone in this industry wants to have good, predictive biomarkers for human diseases. We've wanted that for a very long time, though, and in most cases, we're still waiting. [For those outside the field, a biomarker is some sort of easy-to-run test that for a factor that correlates with the course of the real disease. Viral titer for an infection or cholesterol levels for atherosclerosis are two examples. The hope is to find a simple blood test that will give you advance news of how a slow-progressing disease is responding to treatment]. Sometimes the problem is that we have markers, but that no one can quite agree on how relevant they are (and for which patients), and other times we have nothing to work with at all.
A patient's antibodies might, in theory, be a good place to look for markers in many disease states, but that's some haystack to go rooting around in. Any given person is estimated, very roughly, to produce maybe ten billion different antibodies. And in many cases, we have no idea of what ones to look for since we don't really know what abnormal molecules they've been raised to recognize. (It's a chicken-and-egg problem: if we knew what those antigens were, we'd probably just look for them directly with reagents of our own).
So if you don't have a good starting point, what to do? One approach has been to go straight into tissue samples from patients and look for unusual molecules, in the belief that these might well be associated with the disease. (You can then do just as above to try to use them as a biomarker - look for the molecules themselves, if they're easy to assay, or look for circulating antibodies that bind to them). This direct route has only become feasible in recent years, with advanced mass spec and data handling techniques, but it's still a pretty formidable challenge. (Here's a review of the field).
A new paper in Cell takes another approach. The authors figured that antigen molecules would probably look like rather weirdly modified peptides, so they generated a library of several thousand weirdo "peptoids". (These are basically poly-glycines with anomalous N-substituents). They put these together as a microarray and used them as probes against serum from animal models of disease.
Rather surprisingly, the idea seems to have worked. In a rodent model of multiple sclerosis (the EAE, or experimental autoimmune encephalitis model), they found several peptoids that pulled down antibodies from the model animals and not from the controls. A time course showed that these antibodies came on at just the speed expected for an immune response in the animal model. As a control, another set of mice were immunized with a different (non-disease-causing) protein, and a different set of peptoids pulled down those resulting antibodies, with little or no cross-reactivity.
Finally, the authors turned to a real-world case: Alzheimer's disease. They tried out their array on serum from six Alzheimer's patients, versus six age-matched controls, and six Parkinson's patients as another control, and found three peptoids that seems to have about a 3-fold window for antibodies in the AD group. Further experimentation (passing serum repeated over these peptoids before assaying) showed that two of them seem to react with the same antibody, while one of them has a completely different partner. These experiments also showed that they are indeed pulling down the same antibodies in each of the patients, which is an important thing to make sure of.
Using those three peptoids by themselves, they tried a further 16 AD patient samples, 16 negative controls, and 6 samples from patients with lupus, all blinded, and did pretty well: the lupus patients were clearly distinguished as weak binders, the AD patients all showed strong binding, and 14 out of the 16 control patients showed weak binding. Two of the controls, though, showed raised levels of antibody detection, up to the lowest of the AD patients.
So while this isn't good enough for a diagnostic yet, for a blind shot into the wild blue immunological yonder, it's pretty impressive. Although. . .there's always the possibility that this is already good enough, and that the test picked up presymptomatic Alzheimer's in those two control patients. I suppose we're going to have to wait to find that out. As you'd imagine, the authors are extending these studies to wider patient populations, trying to make the assay easier to run, and trying to find out what native antigens these antibodies might be recognizing. I wish them luck, and I hope that it turns out that the technique can be applied to other diseases as well. This should keep a lot of people usefully occupied for quite some time!