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Amyloid and Neurodegeneration Have Different Underlying Genetics

Series - Alzheimer's Association International Conference 2016:

When pursuing an elusive beast, hunters look for the traces it leaves behind as clues to its whereabouts. Geneticists are employing a similar method to hunt variants linked to Alzheimer’s disease, with changes in the brain representing the variants’ traces. By correlating biomarker changes with genetic factors, researchers gain clues to the mechanism of action of these genes. The method can also bump rare genes, or genes with small effects, above the line of genome-wide significance in genome-wide association studies (GWAS). At the Alzheimer’s Association International Conference 2016, held July 22-28 in Toronto, several scientists described the use of PET and MRI data to identify genes involved in pathology.

A common theme emerged when various groups reported finding distinct sets of factors that influenced amyloidosis versus tau degeneration. The findings imply that these processes have different underlying causes, researchers noted. Other talks homed in on specific genes involved in atrophy, in some cases analyzing known AD genes for associations, and in others looking for novel genes. To many researchers, the data reinforce that to prevent the progression of AD it will be important to treat not only factors that affect amyloid, but also those that affect neurodegeneration.

Amyloid and Atrophy March to Different Drummers
Previous data have long identified a disconnect between amyloid and atrophy. The regions affected by each form distinct, though overlapping, patterns in the brain. In addition, many older people have brain atrophy without amyloid accumulation (see Sep 2015 conference news; Sep 2015 news; Aug 2016 conference news). 

Prashanthi Vemuri of the Mayo Clinic in Rochester, Minnesota, wondered if amyloid and atrophy might involve distinct risk and protective factors. To test this idea, she analyzed data from Mayo Clinic Study of Aging participants aged 70-90. The cohort comprised 713 cognitively healthy controls, 148 people with mild cognitive impairment, and 12 with AD dementia. Vemuri looked for demographic and health factors that associated with either global amyloid load as measured by PiB PET scans, or thickness of the entorhinal cortex and inferior and middle temporal cortices as seen with structural MRI.

For amyloidosis, as expected, older age and the presence of an ApoE4 allele heightened risk. Being a man, or having ApoE2, protected against plaques. However, little else affected amyloid deposition. The only other significant association Vemuri found was that high levels of cholesterol and other lipids in the blood at midlife increased risk. In contrast, many factors contributed to atrophy. Lifestyle choices such as smoking associated with brain shrinkage, as did numerous chronic diseases of aging, such as hypertension and diabetes. Vemuri combined 19 medical issues, including cardiovascular disease, diabetes, metabolic syndrome, obesity, and mental illness, into a single Multiple Chronic Conditions (MCC) score. MCC scores associated with atrophy. Men had higher MCC scores than women on average, and also lost more brain volume with age. Curiously, education and cognitive activity did not protect against either amyloid accumulation or atrophy in this cohort.

The data argue that Alzheimer’s progression is more complex than simply amyloidosis driving tangles that in turn drive atrophy, Vemuri said. Instead, different factors affect each process. She tweaked the common AD analogy that amyloid acts as the gun and tau the bullet by saying that amyloid is the gun and degeneration the bullet. The speed of the bullet varies, Vemuri believes, based on risk factors that have nothing to do with amyloid. Other AAIC talks addressed how genetic variants may underpin some of these factors.

How do tau tangles fit in? Neurodegeneration has often been thought of as synonymous with tangles, but tau PET imaging data has now made clear that the brain can shrink without any tangles present (see Aug 2016 conference news). To specifically compare risk factors for amyloidosis, for tangles, and for atrophy, Vemuri analyzed a smaller cohort of 326 cognitively normal participants who had undergone tau imaging with the tracer AV1451. She found that amyloidosis was the main factor driving tau pathology, in agreement with recent imaging studies (see Jul 2016 news; Aug 2016 conference news). In turn, tangles drove some atrophy. However, here, too, Vemuri calculated that MCC scores affected neurodegeneration independently of amyloid or tau deposits. “Non-AD pathways contribute significantly to AD-pattern neurodegeneration,” she concluded. She is currently investigating what those pathways might be.

Others found the data plausible. “It is not terribly surprising that other mechanisms, and perhaps those related to chronic illnesses, can lead to atrophy independent of amyloid. This implies that, in addition to anti-amyloid therapies, we need to look beyond amyloid for mechanistic pathways that lead to atrophy or tau pathology when developing new treatments for AD,” Adam Fleisher at Eli Lilly, Indianapolis, wrote to Alzforum.

Distinct Gene Sets For Amyloid and Atrophy
Some clues as to what those distinct pathways might be came from Michel Grothe of the German Center for Neurodegenerative Diseases (DZNE), Rostock. First, Grothe and colleagues delineated patterns of brain amyloid and atrophy in a cohort of 75 AD patients compared to 126 controls, using florbetapir PET and structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This analysis provided a graduated pattern of how amyloid and atrophy susceptibility varied across the entire brain, Grothe said. He then compared these patterns to gene expression data from the Allen Brain Institute’s Atlas of the Adult Human Brain Transcriptome (see Hawrylycz et al., 2012). This database contains gene-expression profiles from six middle-aged people who died without brain pathology, and is distinguished by dense sampling from hundreds of regions across each brain. The researchers looked for functional gene sets whose regional expression patterns coincided with the graded patterns of amyloid deposition and atrophy they had found in the DZNE cohort.

They found distinct gene-expression differences in areas vulnerable to amyloid versus atrophy. The amyloid-prone regions of the control brains expressed low levels of genes involved in protein synthesis and mitochondrial respiration, while atrophy-prone areas expressed high levels of genes responsible for axon and dendrite growth and responses to extracellular signaling. For the atrophy pattern, some of the strongest individual gene associations within the overexpressed gene sets were with the tau gene MAPT and the tau kinases CDK5 and MAPK1/ERK2, Grothe told Alzforum.

These data suggest testable hypotheses, Grothe said. For example, perhaps the high rate of aerobic glycolysis that has been described for amyloid-vulnerable regions explains why mitochondrial respiration genes are scarce there (see Sep 2010 news). A relative lack of protein synthesis genes might lead to more protein-folding errors and accumulation of misfolded debris in amyloid-prone regions. Meanwhile, the high expression of dendrite growth genes in atrophy-susceptible regions suggests those areas have high synaptic plasticity, Grothe said. The medial temporal lobe is one of the most plastic neuronal systems in the human brain and also has the highest susceptibility to tau pathology (see Walhovd et al., 2016). Its high capacity for continued synaptic reorganization may come with the downside of a higher susceptibility to tau hyperphosphorylation and associated neurodegeneration as the brain ages, Grothe suggested.

The data complement a recent paper from researchers at the University of Cambridge who also correlated gene expression data from the Allen Brain Atlas with regions vulnerable to AD (see Aug 2016 news). The U.K. researchers took a slightly different approach. They focused on those areas known to accumulate tangles as described in the Braak staging scheme, and specifically looked for expression changes in genes such as chaperones and proteases that regulate Aβ or tau accumulation. They found high expression of genes that promote Aβ or tau production and aggregation, and low expression of genes that prevent it, in the vulnerable regions. On an individual gene level, they also found high expression levels of the MAPT gene in tau-susceptible regions, agreeing with Grothe’s data. Together, these studies highlight that the accumulation of plaques and tangles in specific regions may be due to underlying susceptibilities in the local cells, and that the cellular mechanisms conveying this susceptibility may differ for amyloid versus tau accumulation.

Andre Altmann of University College London presented additional data emphasizing the different risk factors for amyloidosis versus atrophy. Altmann and colleagues calculated a genome-wide polygenic score (GPS) for AD risk based on data from the International Genomics of Alzheimer’s Disease database of more than 54,000 participants. The score was based on the presence and number of alleles at up to 150,000 genetic loci across the entire genome, excluding ApoE, and included a weighting factor that took into account how much each variant increased risk. The researchers then applied this score to the ADNI cohort, looking for associations with amyloid or atrophy changes over two years.

In 844 participants who had undergone florbetapir PET, the researchers found no link between their polygenetic risk score and global amyloid accumulation, but did find a strong effect from the ApoE4 allele. In 953 people with FDG PET data, on the other hand, a high polygenic risk score predicted a decline in brain metabolism, mostly in people who had mild cognitive impairment at baseline. The effect was most pronounced in certain brain regions, such as the left temporal pole, and strongest in people who did not carry an ApoE4 allele. The effect of the combined genes was similar in magnitude to that of ApoE4 alone on amyloidosis. Overall, the data highlight that distinct drivers exist for amyloid deposition and atrophy, Altmann said. ApoE4 mainly drives amyloidosis, while many genes affect atrophy, he concluded.

A previous study reported that polygenic risk scores associated with both faster memory decline and higher amyloid load in the ADNI cohort (see Jul 2016 news). Other studies have tied GWAS hits to APP processing (see Apr 2015 conference news; Nov 2015 conference news). It may be that a person’s amyloid changes over two years are not pronounced enough to show an association with their GPS in this study, Altmann suggested.

Genes and Brain Volume
Other talks zoomed in on specific genes that affect atrophy as measured by brain volume. Tugce Duran and Liana Apostolova of the Indiana University School of Medicine, Indianapolis, investigated the associations of 27 variants from the top 20 Alzheimer risk genes with atrophy in the medial temporal lobe. The researchers used ADNI data from 441 cognitively normal controls, 764 people with mild cognitive impairment, and 294 with AD. They found different associations in each group. Among controls, only SLC24A4/RIN3—a gene that plays a role in lipid metabolism and has been associated with hypertension in African-Americans—correlated with atrophy (see Jul 2013 news). In the MCI group, SLC24A4 and the zinc finger gene ZCWPW1 turned up as significant. In people with AD, however, a different set of genes popped up: ABCA7, EPHA1, and INPP5D. The first of these associates with lipid metabolism and amyloid, the latter two with inflammation. “The influence of genes may be confined to specific disease stages,” Duran said. This may be because biomarker changes are limited to certain disease stages, Apostolova noted. “For example, one would expect amyloid-related genes to have an early effect and neurodegeneration genes to show a late effect,” she wrote to Alzforum.

Jake Vogel of McGill University, Montreal, turned up a different genetic association with atrophy. Working with Sylvia Villeneuve at McGill, he examined the relationship between atrophy and 15 GWAS SNPs that were present in the PREVENT-AD cohort. This Canadian study tracks biomarker changes in cognitively normal middle-aged people at risk of late-onset AD due to family history (see Ritchie and Ritchie, 2012). Vogel examined data from 271 participants averaging 62 years old, about one-third of whom carried an ApoE4 allele. Participants lost volume in several brain regions, including subregions of the default mode network (DMN), as they approached the age at which their parents had developed AD. These regions dwindled most in those who carried a harmful BIN1 allele, hence BIN1 may play a role in the DMN’s vulnerability, Vogel concluded. Previous studies have suggested both that BIN1 may bind tau and that it increases Aβ production, leaving its potential mechanism of action in the DMN obscure (see Aug 2012 conference news; Apr 2015 conference news; Nov 2015 conference news). 

Rather than working with known genes, Marco Lorenzi of University College London used a joint model of brain atrophy and SNPs to find new genes. Lorenzi and colleagues compared more than one million SNPs to cortical and subcortical thinning in a cohort of 639 ADNI participants comprising healthy controls and AD patients. He found two distinct sets of genes that associated with volume changes in different brain regions. Cortical thinning in the hippocampus, amygdala, temporal and cingulate cortices associated with SNPs near or within the ADAM23, NAT2, ADAMTSL1, ANK3, NAV2, and CALCOCO1 genes. Many of these genes are involved in cell growth, adhesion, and axon guidance. Meanwhile, shrinking volume in the parahippocampal gyrus and subiculum was associated with variants near ADCY9, APOC1, APOE, PVRL2, and TOMM40, many of which relate to amyloid accumulation and nervous system development. In an independent ADNI MCI group, several of these SNPs discriminated between people who remained stable and those who progressed to AD, Lorenzi said. The findings help link brain atrophy to biological functions and suggest directions for future research, he added.—Madolyn Bowman Rogers

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References

News Citations

  1. Suspected Non-Alzheimer Pathophysiology: It’s Not Exactly a Snap
  2. When There's No Amyloid, It’s Not Alzheimer’s
  3. Staging of Alzheimer’s, the Second: Neurodegeneration Does Not Equal Tauopathy
  4. Do Temporal Lobe Tangles and Cortical Plaques Together Bring on Alzheimer’s?
  5. Tau PET Studies Agree—Tangles Follow Amyloid, Precede Atrophy
  6. Brain Aβ Patterns Linked to Brain Energy Metabolism
  7. Aggregation-Prone Gene Expression Signature Mapped in Brain
  8. Are Early Harbingers of Alzheimer’s Scattered Across the Genome?
  9. The Feud, Act II: Do Alzheimer’s Genes Affect Amyloid or Tau?
  10. Alzheimer’s GWAS Hits Point to Endosomes, Synapses
  11. Pooled GWAS Reveals New Alzheimer’s Genes and Pathways
  12. GWAS Mega-Meta Yields More Risk Genes, BIN1 Binds Tau?

Paper Citations

  1. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL, Abajian C, Beckmann CF, Bernard A, Bertagnolli D, Boe AF, Cartagena PM, Chakravarty MM, Chapin M, Chong J, Dalley RA, Daly BD, Dang C, Datta S, Dee N, Dolbeare TA, Faber V, Feng D, Fowler DR, Goldy J, Gregor BW, Haradon Z, Haynor DR, Hohmann JG, Horvath S, Howard RE, Jeromin A, Jochim JM, Kinnunen M, Lau C, Lazarz ET, Lee C, Lemon TA, Li L, Li Y, Morris JA, Overly CC, Parker PD, Parry SE, Reding M, Royall JJ, Schulkin J, Sequeira PA, Slaughterbeck CR, Smith SC, Sodt AJ, Sunkin SM, Swanson BE, Vawter MP, Williams D, Wohnoutka P, Zielke HR, Geschwind DH, Hof PR, Smith SM, Koch C, Grant SG, Jones AR. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012 Sep 20;489(7416):391-9. PubMed.
  2. Walhovd KB, Westerhausen R, de Lange AM, Bråthen AC, Grydeland H, Engvig A, Fjell AM. Premises of plasticity - And the loneliness of the medial temporal lobe. Neuroimage. 2016 May 1;131:48-54. Epub 2015 Oct 24 PubMed.
  3. Ritchie CW, Ritchie K. The PREVENT study: a prospective cohort study to identify mid-life biomarkers of late-onset Alzheimer's disease. BMJ Open. 2012;2(6) PubMed.

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