Potter R, Patterson BW, Elbert DL, Ovod V, Kasten T, Sigurdson W, Mawuenyega K, Blazey T, Goate A, Chott R, Yarasheski KE, Holtzman DM, Morris JC, Benzinger TL, Bateman RJ.
Increased in vivo amyloid-β42 production, exchange, and loss in presenilin mutation carriers.
Sci Transl Med. 2013 Jun 12;5(189):189ra77.
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The study by Potter et al. on amyloid-β production measured in vivo in PS1 and PS2 carriers is a clever combination of clinical observations, imaging studies, physics, and computational compartmental modeling to better quantify the "biology" of amyloid metabolism in patients. The clinical data suggest a tight regulation of Aβ42 over Aβ40 production, resulting in only a 25 percent increase in the ratio and an 18 percent increase in the absolute production rate of Aβ1-42 in carriers versus non-carriers. Also, the rate constant for "irreversible loss" (the authors mention loss to bloodstream, degradation, and amyloid plaque incorporation) is slightly enhanced for the Aβ42 versus Aβ40 in the PS carriers, suggesting that the human brain has developed an efficient way to sequester Aβ42 from its (toxic?) effects.
It would be interesting to get an idea about the values of soluble Aβ38, 40, and 42 levels from the model in Figure 2 of the paper, based upon how the model fits with the kinetic parameters. This could then be compared to the levels of soluble Aβ42 in many preclinical mouse models, where usually much larger increases (such as a 30-fold in the Tg2576 or even 90-200-fold in the 5xFAD mouse model) of soluble Aβ42 compared to non-transgene controls are observed (Oakley et al., 2006). The devil is in the details, and maybe in the transgenic mouse models, although they have the right directionality, an increase in Aβ42 might not have the right effect size because they don’t have the same adaptive "degradation or sequestration" pathway (remember, the APP transgene is usually human and therefore exogenous to the mouse environment). This could explain why so many β amyloid interventions clearly work in "mouse" backgrounds, because they reduce the artificially massive soluble Aβ production but do not work in the human situation where a number of compensatory processes are already in place to help regulate this overproduction. One possible means to investigate this is to develop quantitative systems pharmacology-based computer models that can be "humanized" using the kind of study described here by Potter et al. The combination of the best of preclinical biology and clinical data would hopefully improve translation and guide better drug discovery.
A similar example has recently been described for the phosphodiesterase 10A target in schizophrenia, where inhibitors as a standalone work in most preclinical animal models but not in human patients. Because in this case we have imaging studies of free striatal dopamine in humans and rodents, the species difference can likely be traced to the quantitative difference in dopamine increase in schizophrenia versus the amphetamine-induced rodent condition, where dopamine does not rise as much.
Even if the direction of change is correct, the effect size makes all the difference.
Oakley H, Cole SL, Logan S, Maus E, Shao P, Craft J, Guillozet-Bongaarts A, Ohno M, Disterhoft J, Van Eldik L, Berry R, Vassar R.
Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer's disease mutations: potential factors in amyloid plaque formation.
J Neurosci. 2006 Oct 4;26(40):10129-40.
Potter and colleagues demonstrate clearly, in vivo, two phenomena we, as a field, have suspected for 10-15 years: Presenilin mutations cause an increase in Aβ42 in the human CNS, and some compartment, presumably early deposits, start to extract Aβ42 from the interstitial fluid long before the disease is manifest, and even before amyloid imaging can visualize deposits. The detailed effects on APP metabolism should be interpreted with regard to the detailed description of these effects in vitro (see Chávez-Gutiérrez et al., 2012). Clearly, the SILK setup developed at Washington University offers a high-definition system for developing an understanding of the precise mechanisms by which drugs modulate APP/Aβ metabolism.
Chávez-Gutiérrez L, Bammens L, Benilova I, Vandersteen A, Benurwar M, Borgers M, Lismont S, Zhou L, Van Cleynenbreugel S, Esselmann H, Wiltfang J, Serneels L, Karran E, Gijsen H, Schymkowitz J, Rousseau F, Broersen K, De Strooper B.
The mechanism of γ-Secretase dysfunction in familial Alzheimer disease.
EMBO J. 2012 May 16;31(10):2261-74.
One of the remarkable aspects of this work, in our view, is the strong evidence it provides for how the fundamental physical characteristics of proteins in biological environments, such as their concentration, influence intricate biological events leading to the onset of disease. Findings of this type are particularly exciting, since they raise the possibility of applying methods established for the study of small-molecule reactions, such as chemical kinetics, to significantly more complex biological processes. We feel that elucidating the connections between physical characteristics and biological consequences in the context of pathological protein aggregation is absolutely crucial for understanding the basis for neurodegeneration. The study by Potter et al. represents a key advance in this direction.
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