Peng S, Zeng L, Haure-Mirande JV, Wang M, Huffman DM, Haroutunian V, Erlich M, Zhang B, Tu Z. Transcriptomic changes highly similar to Alzheimer’s disease are observed in a subpopulation of individuals during normal brain aging. bioRxiv, July 13, 2021 bioRxiv.

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  1. There are many useful findings in this study, such as differences in gene expression involving energy metabolism and synapse pathways as indicators of healthy aging vs. “escalated” inflammation/immune responses in AD, and the presence of “AD-like” gene expression signatures in subsets of young (40 to 70 years of age) individuals.

    Additional steps are needed to determine if those expression signatures are deterministic of the development of AD, or the age of onset of AD, and/or what environmental factors synergize to induce the neuronal dysfunction that results in cognitive loss. In addition, it would be interesting to know how these gene expression profiles correlate with the AD subtypes reported by several of the authors earlier this year (Neff et al., 2021). 

    References:

    Neff RA, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song WM, Li A, Castranio EL, Tcw J, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich ME, Katsel P, Schadt E, Cai D, Brennand KJ, Haroutunian V, Zhang B. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv. 2021 Jan;7(2) Print 2021 Jan PubMed.

    View all comments by Andrea Tenner

  2. First and most important, kudos to the authors for taking on the project. It's a great concept and we end up with a really useful dataset that others can use to test ideas about AD ... and aging.

    From the methodological point of view, I do have some reservations. The first is unavoidable. Our genetic resources are neither diverse nor representative. So their conclusions basically apply only to white Caucasian aging and AD. The second is more subtle. The brain regions sampled are not uniform across the CNS. It's quite interesting that the more "primitive" subcortical regions (like spinal cord) don't show a strong aging signature. But they are poorly represented in the AD cohorts. Also, the hypothalamus has my attention recently because of some lovely work on metabolism and aging by Suzana Herculano-Houzel. She identified it as a key to aging and according to Peng et al. it was the brain area with the most up-/downregulated genes of all. The problem is that it only appears in one of the aging sets (GTEx Aging) and none of the others. That is unfortunate and raises the question of whether their analysis compensated appropriately for the heterogeneity. (Also, why does no one seem to care about the amygdala?)

    The analysis itself also makes me nervous. They look for genes that are consistently changed with age or disease. First of all, their criteria are pretty loose (change was required in only four of 19 datasets). Then they do pathway analysis of these genes. I get it, but I confess to be thinking here that the glass is half-empty (see variability comments below). I was pleased that they looked at specific regions to see if there were regional differences in aging/AD signatures. But it's not clear to me how valid the comparisons are if a brain region (for example, hypothalamus) is not in all of your datasets. And statistically valid does not always mean biologically meaningful.

    Also, continuing in my role as crotchety curmudgeon, the global changes that they report don't tell us much that we didn't already know. Neuronal proteins go down; immune system proteins go up. A few items such as the insulin/lipid data are interesting, but not followed up. The single genes they cite could be interesting, but from what they've shown us we can't be sure.

    One of the things that I take away from the entire exercise is that both AD and aging, even in this fairly homogeneous cohort, are incredibly variable. Figure 3 is a great example. Of all the genes in the four datasets graphed, only five upregulated and 15 downregulated genes were common to all four. By far the biggest numbers in the Venn diagrams are the single-color (one dataset) regions. The same can be said for the AD datasets. They focus their discussion on the more closely aligned GTX and UK datasets in their discussion, but to me the numbers still are not that impressive and excluding the others basically misses the point of the entire exercise. The other datasets should be like validation sets, but they don't seem to validate very strongly.

    View all comments by Karl Herrup

  3. The authors extracted age signatures from healthy brain studies and compared them to disease signatures from AD case-control studies. They observed overlap between these signatures, and were able to cluster some cognitively healthy individuals based on their similarity with AD signatures.

    As the authors suggest, this similarity probably indicates that these individuals were on the path toward a late-onset AD (LOAD) phenotype, and what we observe are the earlier stages thereof. This is in line with the idea that the disease begins long before the first clinical complaints, which also pans out in imaging or fluid biomarker studies. It is of interest that these healthy brains do not yet show the common pathological changes of AD, suggesting that these (milder?) regulation changes precede pathology. Of course, what we really want to know is what determines if an individual ends up on the AD path, which might have been decided earlier than we previously thought, given that the changes can be seen in some people as young as 45 years of age.

    What this means for the biology of the AD brain is of interest. I think it makes sense that we observe typical aging signatures in AD brain, for example we expect accumulation of damage and decreased function in a normal brain over time. Certain processes will change, such as the activity of immune processes as they clear damaged cells or other aggregations from the brain in an attempt to boost neuron function, which slowly diminishes with age. When AD starts, these same processes are also active, trying to deal with the situation in the brain, in perhaps a similar way to what occurs, albeit more slowly, in the aging process.

    In our studies of postmortem dementia brains, we see these natural aging processes in AD brains, but also in other forms of dementia or in brains undergoing some other form of neurodegeneration. We consider these processes "specific" for those diseases. They are a consequence of the brain state, whereas we typically want to know what specifically causes the start of these diseases, because that is what we want to prevent or halt. This study may help us identify these "normal" non-specific changes that occur in diseased or aging brains, and separate them from the other changes that occur in specific disease states, providing much clearer views of what happens there. This study is very helpful from that perspective.

    View all comments by Jeroen van Rooij

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  1. Do Gene Expression Signatures of Aging Signal AD?