‘Proteogenomics’ Ties Genes That Control 38 Proteins to Alzheimer’s

Identifying genes that control protein levels could provide insight into disease development and progression. However, genetic analyses such as genome-wide associated studies identify regions associated with certain traits, not necessarily causal genes. To find the latter, scientists led by Carlos Cruchaga at Washington University, St. Louis, have linked genetic variants to more than 6,000 proteins in 3,500 cerebrospinal fluid samples. The goal? To pinpoint functional genes directly involved in Alzheimer’s disease. The scientists integrated AD GWAS with protein quantitative trait loci, i.e., variants that correlate with the concentrations of proteins in the largest study of its kind. Published November 11 in Nature Genetics, the work identified three hotspots in the genome that regulate levels of hundreds of proteins. Of them, 38 might drive AD pathology, 24 are new to the field.

  • Combo of GWAS and protein quantitative trait loci infers genes and proteins linked to AD.
  • Of 38 proteins identified, most are made in microglia, macrophages, astrocytes.
  • Almost half are targets of existing drugs.

“This is a tour de force paper from the Cruchaga team, with impressive scope in terms of both the number of protein aptamers tested and the sample size,” wrote Niklas Mattsson-Carlgren of Lund University in Sweden, who was not involved in the study. Aptamers are short, single-stranded nucleic acids that each fold and bind to specific proteins, much like antibodies do to antigens. The researchers used more than 7,000 of them.

Previous studies have traced changes in gene expression to specific genetic variants, or expression quantitative trait loci (Vösa et al., 2021; de Klein et al., 2023). However, cells regulate protein synthesis, degradation, and post-translational modification, hence expression alone does not always track with protein levels, making it difficult to interpret the effect of GWAS variants through eQTLs alone. “Proteins are the closest thing to the disease status,” Cruchaga told Alzforum. “They capture the overall health of the system.” Enter protein quantitative loci.

To find pQTLs, Cruchaga and colleagues scoured the cerebrospinal fluid for proteins that are genetically regulated. In 2021, they had reported that more than 70 per cent of the brain proteome ends up in CSF, making this fluid a useful mirror of parenchymal biology (Jul 2021 news). For the current study, first author Daniel Western and colleagues created an atlas of pQTLs and correlated them with CSF proteomes in healthy controls and in people with AD. They found that 3,885 pQTLs, spread across the genome, regulate 1,883 CSF proteins (image below). Three-quarters of these associations were new.

PQTLS. In this Manhattan plot, loci across the genome correlate with levels of CSF proteins. [Courtesy of Western et al., 2024.]

Most pQTLs associated with one or two proteins, but three hotspots contained numerous epQTLs that had dramatic effects, each regulating 50 or more proteins (image below). One hotspot, on chromosome 19q13.32, spans the notorious APOE locus. The others were chr3q28 and chr6pp22.2-21.32.

Most of the pQTLs identified in these three hotspots were trans as opposed to cis. In other words, they regulate genes far from its hotspot, not adjacent to it. “We often think that genetic regulation only happens in cis, but clearly at the protein level, it is much more complex,” Cruchaga said.

Pleiotropic Hotspots. Dozens to hundreds of pQTLs at chr3q28 (left), chr6p22.2-21.32 (middle), and chr19q13.32 (right), associated with levels of 208, 70, and 335 CSF proteins, respectively. [Courtesy of Western et al., 2024.]

Of the three hotspots, the APOE locus associated with most CSF proteins—355 in all. “We know APOE is a strong AD risk factor, but we did not expect to see that many associations,” Cruchaga said. Eleven variants in this region associated with the 335 proteins, most of which are produced in astrocytes but mainly found in neurons, suggesting regulated cell-to-cell communication. The APOE4 and APOE2 variants, which raise and lower a person’s chance of getting AD, respectively, associated with 205 and 49 of them. Only four of these proteins were cis. Some of the proteins, such as NfL and 14-3-3, are markers for AD or other neurodegenerative diseases while others, including calcineurin, associate with Alzheimer’s pathology (Panda et al., 2024; Dec 2024 news; May 2024 news; Hopp et al., 2018). APOE transports a variety of lipids between cells and organelles.

The hotspot at chr3q28 sits between the GMNC and osteocrin (OSTN) genes. They are involved in DNA replication and dendritic arborization, respectively. The 208 proteins linked to this region, all trans, were mostly found in neurons. Pathway analyses indicated their involvement in axon development, synapses, and cell junctions.

Many of these 208 associated with brain morphology, such as volume and surface area, confirming previous pQTL study led by Mattsson-Carlgren and Oskar Hansson, also at Lund University. They had identified the chromosome 3 locus as a driver of CSF protein levels (Hansson et al., 2023; Karlsson et al., 2024). “Since the variation in CSF protein levels are not specific to AD cases, the OSTN-region may have broad and important upstream biological effects on brain structure and/or CSF metabolism regardless of disease,” Mattsson-Carlgren said.

The chr6pp22.2-21.32 hotspot, which lies within the human leukocyte antigen region (HLA), associated with 70 proteins. These were highly immune-specific and included components of the complement system and those involved in antigen processing and presentation. Many are produced in microglia. “HLA is a very complex region to understand because a lot of recombination has happened there,” Cruchaga said. Variants in this region often regulate genes in other locations of the chromosomes involved in immune response and inflammation. Interestingly, this same region associated with levels of 1,756 proteins in plasma. “This data indicates that immune response is very important for healthy brains, but the specific proteins that are more relevant to the brain is difficult to disentangle at this point,” Cruchaga wrote to Alzforum.

What of AD?
PQTL atlas at hand, the scientists asked if they could tie proteins to specific diseases, such as AD. They married GWAS and proteomic results using three methods: proteome-wide association, Mendelian randomization, and co-localization analyses. PWAS used pQTLs to identify genetically controlled protein levels associated with AD. Mendelian randomization determined whether changes in protein levels associated with AD risk at the population level. And the co-localization assessed if AD GWAS risk loci overlap with pQTLs for those proteins.

This generated 38 CSF proteins that showed up in at least two of the analyses. Of these, 24 had not been associated with AD previously. These 38 are mostly produced in microglia, macrophages, and astrocytes, and are involved in immune pathways and neurodegeneration, including AD.

Noteworthy among the 38 were immune proteins. TREM2 contains an immunoreceptor tyrosine-based activation motif (ITAM) that complexes with the signaling adaptor DAP12 to activate microglia (Samuels et al., 2023). CD33 and PILRA70 contain the inhibitory counterpart, ITIM, which tempers microglial immune responses (image below). In all, 16 of the 38 proteins modulate microglial responses. TREM2, angiotensin-I-converting enzyme (ACE), and progranulin (GRN) were inversely associated with AD. Cathepsin H (CTSH), SHANK-associated RH domain interactor, and complement C3b-C4b receptor (CR)1 directly associated.

Alzheimer’s Immune Axis. Sixteen AD-associated proteins (bold labels) modulate immune responses via microglia signaling pathways. [Courtesy of Western et al., 2024.]

The scientists found a pQTL for TREM2 at the MS4A gene family locus, confirming its association with AD pathogenesis (Deming et al., 2019). “When we saw that the major regulator of TREM2 is MS4A, we knew we had biological context,” Cruchaga said. Scientists first linked MS4A to AD risk 23 years ago, but the functional variant or mechanisms by which this lipid-sensing gene family influences AD risk were murky (Naj et al., 2011). “This larger study allowed us to demonstrate that there are two independent signals in this region, one driven by MAS4A4A and other by MS4A6A,” Cruchaga wrote to Alzforum.

The researchers also identified proteins associated with endolysosomal pathways, including CTSH, cystatin 8 (CST8), and ceroid-lipofuscinosis neuronal protein 5 (CLN5). The lysosomal proteins progranulin and TMEM106b were previously linked to frontotemporal dementia (Gass et al.,  2006; Van Deerlin et al., 2010); they also appear to be regulated by pQTLs. That scientists had previously associated parenchymal ACE and CTSH levels with AD emphasizes how informative CSF can be for these types of studies, Cruchaga et al., claimed (Wingo et al., 2021).

Could these data be useful in the clinic? Fifteen of the 38 proteins are targets of currently used drugs, including the chemotherapy agent cetuximab, and the ACE inhibitor captopril. “Not only are these proteins candidate drug targets, but they could be great diagnostic markers,” Nicholas Seyfried of Emory University, who was not involved in the study, told Alzforum.

They might also be useful for diagnosis. For example, Western and colleagues used their aptamers to develop a proteomic risk score for AD, and in preliminary modelling, it distinguished people with disease from controls better than did a polygenic risk score. Mattsson-Carlgren found that intriguing. “It would be interesting to compare the AD-predictive model developed in this study with other established biomarkers or less-complex models that might have a higher likelihood of being reproducible,” he said.

Cruchaga and his team plan to expand their analyses to Parkinson’s and beyond. “We are just scratching the surface,” he said. “Proteomics technology is moving fast, and we expect that similar studies will be published in the future, with many more proteins.”—Kristel Tjandra

Kristel Tjandra is a freelance writer in Springfield, Virginia.

Comments

  1. In this study, Western et al. leveraged CSF-based proteomics via the SOMAscan 7k platform and genetic data from approximately 3,500 individuals to conduct the largest investigation of CSF protein quantitative trait loci (pQTLs) to date. This study provides new insights into the genetic regulators of protein levels, which is a particularly compelling extension of existing human genetic data sets, given that most therapeutic approaches target proteins. By integrating pQTLs with AD GWAS data via standard approaches (co-localization, Mendelian Randomization or MR, and protein-wide association studies or PWAS), the authors provide several insights that are valuable for drug discovery, namely nominations for:

    • Genes responsible for AD GWAS signals, to inform target identification (via co-localization)
    • Proteins that are causally associated with AD, thereby nominating disease intervention points and informing target identification (via MR)
    • Directionality of protein levels and genetic associations (e.g., lower protein levels are associated with AD risk), which can inform therapeutic strategies, such as agonism versus inhibition (via MR and PWAS)

    This study nominated 38 proteins using at least two of these methods, while eight proteins were detected by all three (co-localization + MR + PWAS). In most cases, these proteins displayed intuitive relationships to disease. For example, the well-established microglial gene TREM2 was negatively associated with disease risk, i.e., lower protein levels, increased risk, which is expected based on known TREM2 loss-of-function coding variants that increase AD risk (Guerreiro et al., 2013; Jonsson et al., 2013). Moreover, GRN was found to be similarly negatively associated with AD risk. This is also an expected finding given that GRN mutations resulting in progranulin deficiency are associated with frontotemporal dementia (Baker et al., 2006), and a common variant signal driven by a known progranulin-lowering SNP (rs5848) was discovered in a recent AD GWAS (Bellenguez et al., 2022).

    Like TREM2 and GRN, PILRA was nominated across all three pQTL-GWAS integration methods. This is an exciting finding and adds to the emerging evidence that PILRA is the causal gene in the ZCWPW1/NYAP1 AD GWAS locus (Weerakkody et al., Research Square preprint 2024), including 1) the PILRA G78R variant (rs1859788) accounts for AD GWAS signal in this locus, as demonstrated via conditional analysis (Novikova et al., 2021) and 2) a burgeoning genetic connection between this PILRA variant and APOE genotype (Lopatko Lindman et al., 2022; Monroe et al, 2024; Belloy et al., 2023). However, the negative association of PILRA protein levels with AD risk appears to be at odds with the fact that the G78R variant, associated with reduced ligand binding (Rathore, et al., 2018), is associated with reduced AD risk and delayed AD age of onset (He et al., 2021). 

    Several characteristics of this study may reconcile these somewhat counterintuitive observations. First, this study nominated PILRA via pQTL data that only captures SNP effects on protein levels and not metrics of protein activity, for example, ligand binding, receptor signaling, co-receptor interactions, etc. Second, CSF levels of PILRA protein may not correspond to levels in brain. Therefore, while this study is useful in connecting AD GWAS regions to specific proteins and articulating a direction of association, further studies are necessary to understand the mechanistic basis of these connections and confirm their relationship to function.

    Finally, two additional characteristics of the proteomics platform used in this study are important to consider. First, the SOMAscan aptamer-based approach may not be sensitive enough to discern between proteins with a high degree of sequence homology, or between isoforms. Second, as the authors point out, many proteins remain unaccounted for in the current iteration of the SOMAscan assay. We might still be missing protein data for many AD GWAS loci, and there may be even more AD-relevant proteins to be discovered with future versions of the platform. Despite these technical limitations, we find this work to be an exciting step forward for elucidating mechanisms of genetic risk factors and their impact in disease.

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References

News Citations

  1. Paper Alert: pQTLs Pin GWAS Loci to Tissue Proteins, Drug Targets
  2. Do CSF and Plasma NfL Diverge After Alzheimer’s Disease Onset?
  3. Neurofilament Light Clears First Hurdle as Bona Fide FTD Biomarker

Mutations Citations

  1. APOE Region
  2. APOE C130R (ApoE4)
  3. APOE R176C (ApoE2)

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