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Walker KA, Chen J, Shi L, Yang Y, Fornage M, Zhou L, Schlosser P, Surapaneni A, Grams ME, Duggan MR, Peng Z, Gomez GT, Tin A, Hoogeveen RC, Sullivan KJ, Ganz P, Lindbohm JV, Kivimaki M, Nevado-Holgado AJ, Buckley N, Gottesman RF, Mosley TH, Boerwinkle E, Ballantyne CM, Coresh J. Proteomics analysis of plasma from middle-aged adults identifies protein markers of dementia risk in later life. Sci Transl Med. 2023 Jul 19;15(705):eadf5681. PubMed.
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Amsterdam UMC, loc. VUmc
Maastricht University; VU University Medical Centre
Johnson et al. show beautiful analyses of CSF proteomic alterations in relation to estimated year of disease onset (EYO), providing, for the first time, in-depth insight into when, and which, molecular processes are dysregulated in autosomal-dominant AD.
The panel measured with selected reaction monitoring mass spectrometry included 59 proteins, which the authors, in their previous studies, had identified to be altered in brain tissue and to be related to pathology in sporadic AD. Previous CSF studies that investigated associations with EYO were restricted to just a few proteins that were measured with ELISAs.
A particular strength of this study is that the proteins selected seem to have distinct relationships with EYO, which are specific to CSF amyloid, tau, and imaging and clinical outcomes. These results suggest that different processes become abnormal in five different stages of the disease: in the very early stage SPON1 and SMOC1 were increased together with decrease in the Aβ42/40 ratio. These proteins have been reported to be increased in sporadic AD in a number of different studies (e.g., by Sung et al., 2023; Tijms et al., 2023; Visser et al., 2022).
The second group included proteins with higher levels than in noncarriers, which were related to proteins in glycolytic metabolism. This was followed by a third stage, in which tau increased together with cognitive impairment. The fourth stage included proteins related to immune activation, while stage 5 was characterized by decreased levels of other glycolytic proteins and synaptic proteins.
This paper is a milestone, providing a new view as to how AD develops. This will definitely be further refined when more proteins are measured.
Also, because these proteins were selected based on sporadic AD, their relationship with ADAD further validates their role in AD pathogenesis. A major question remains as to how early these proteins change in sporadic AD, where it is not possible yet to define an EYO.
Plasma proteomics may provide further insight, as this fluid is more easily obtained.
Indeed, Walker et al. took this approach to look into an enormous longitudinally followed cohort of more than 10,000 individuals whose cognition was normal at the start of the study. These individuals were followed for more than 25 years. Interestingly, the authors found that protein levels associated with MAPK signaling were increased 25 years before dementia onset. In the Johnson study, these proteins were increased in early AD as well. Although the Walker et al. study did not mention SMOC1, they did find that alterations in proteins related to the extracellular matrix were predictive for future dementia. It would be of major interest to learn how the plasma proteins associate with plasma p-tau and amyloid markers in the same cohort.
References:
Sung YJ, Yang C, Norton J, Johnson M, Fagan A, Bateman RJ, Perrin RJ, Morris JC, Farlow MR, Chhatwal JP, Schofield PR, Chui H, Wang F, Novotny B, Eteleeb A, Karch C, Schindler SE, Rhinn H, Johnson EC, Oh HS, Rutledge JE, Dammer EB, Seyfried NT, Wyss-Coray T, Harari O, Cruchaga C. Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer's disease. Sci Transl Med. 2023 Jul 5;15(703):eabq5923. PubMed.
Tijms BM, Vromen EM, Mjaavatten O, Holstege H, Reus LM, vanderLee SJ, Wesenhagen K, Lorenzini L, Vermunt L, Venkatraghavan V, Tesi N, Tomassen J, denBraber A, Goossens J, Vanmechelen E, Barkhof F, Pijnenburg YA, vanderFlier WM, Teunissen CE, Berven F, Visser PJ. Large-scale cerebrospinal fluid proteomic analysis in Alzheimer's disease patients reveals five molecular subtypes with distinct genetic risk profiles. 2023 May 11 10.1101/2023.05.10.23289793 (version 1) medRxiv.
Visser PJ, Reus LM, Gobom J, Jansen I, Dicks E, van der Lee SJ, Tsolaki M, Verhey FR, Popp J, Martinez-Lage P, Vandenberghe R, Lleó A, Molinuevo JL, Engelborghs S, Freund-Levi Y, Froelich L, Sleegers K, Dobricic V, Lovestone S, Streffer J, Vos SJ, Bos I, ADNI, Smit AB, Blennow K, Scheltens P, Teunissen CE, Bertram L, Zetterberg H, Tijms BM. Cerebrospinal fluid tau levels are associated with abnormal neuronal plasticity markers in Alzheimer's disease. Mol Neurodegener. 2022 Mar 28;17(1):27. PubMed. Correction.
View all comments by Pieter Jelle VisserUniversity of Toronto
In the age of big data, it has become an almost impossible task to genuinely assess papers reporting on thousands of samples, short of repeating the analyses. Although with resource manuscripts like this, the long-term impact can be difficult to gauge initially, the work by Walker et al. is groundbreaking beyond its scale.
In addition to providing testable hypotheses, by putting a spotlight on specific blood proteins, including GDF15 and SERPINA3, the study adds to the concept that the earliest changes in the levels of blood proteins, which may be helpful for dementia risk prediction, can be obscured by the time the disease progresses to dementia.
View all comments by Gerold Schmitt-UlmsWashington University School of Medicine
This new DIAN study is indeed fascinating. There has been prior research suggesting that glycolytic metabolism is specifically altered early in Alzheimer's disease. What makes studies like these in autosomal-dominant Alzheimer's disease highly impactful is their ability to tease apart the timing of different events, including the timing related to metabolism.
It is striking to see that enzymes associated with glycolysis transiently rise in the CSF almost two decades prior to expected symptom onset and again near symptom onset. I agree with the authors that other studies will be needed to understand why this is occurring and in what cells. It is also difficult to know how CSF proteins reflect metabolic flux in the brain. However, these data suggest some intriguing possibilities.
The first rise in glycolytic proteins appears to occur just as amyloid plaques start developing and coincides with a transient period of improved cognition. There are several ways to potentially explain this, but to me this parallels studies in mouse models where soluble amyloid is associated with neuronal hyperexcitability. In contrast, the second rise in glycolytic enzymes occurs closer to symptom onset, which might reflect various processes including a glial response as suggested by the authors.
Regardless, I think this study strongly supports the need to investigate brain metabolism not only as a result of neurodegeneration, but potentially as a key element in the pathogenesis of Alzheimer's disease.
View all comments by Manu GoyalBoston University School of Medicine
This ADAD CSF proteomic study is an asset to the AD community as it’s based on a collection of samples over the course of six decades. The authors showed changes in proteins from CSF associated with biomarker and pathological changes of AD. Especially protein changes in the prodromal stage of the disease and patterns of target pathways are of interest.
The first target pathway highly associated with Aβ plaque in AD 30 years before disease onset is the “matrisome,” which has been previously identified as one of the major pathways in AD and APOE4 AD human brain in transcriptome (TCW et al., 2022) and proteome (Johnson et al., 2022). Within this category, the study highlighted two novel proteins, SMOC1 and SPON1, potentially involved in Aβ plaque formation. While SPON1 is temporarily activated for five to seven years, SMOC1 stays activated before and after disease onset, which could be promising as the earliest-detectable biomarker for AD.
After matrisome activation, it is also interesting to see that glycolytic metabolism and stress response pathways display pulsing patterns. Gycolytic metabolism especially pulses twice; once after matrisome and another right on the clinical onset when the immune activation pathway is elevated, indicating glial activation. Future studies on the potential connection between metabolic changes and neuronal stress response and glial activation and their mechanistic study can provide a better resolution of these temporal responses.
This study is a great reference for LOAD associated with amyloid and tau changes. For LOAD cases, when picturing our future, we can identify biomarkers isolating the risk group who requires treatments multiple decades earlier to prevent the disease, expecting a drug that we can easily take to prevent amyloid formation.
References:
Tcw J, Qian L, Pipalia NH, Chao MJ, Liang SA, Shi Y, Jain BR, Bertelsen SE, Kapoor M, Marcora E, Sikora E, Andrews EJ, Martini AC, Karch CM, Head E, Holtzman DM, Zhang B, Wang M, Maxfield FR, Poon WW, Goate AM. Cholesterol and matrisome pathways dysregulated in astrocytes and microglia. Cell. 2022 Jun 23;185(13):2213-2233.e25. PubMed. BioRxiv.
Johnson EC, Carter EK, Dammer EB, Duong DM, Gerasimov ES, Liu Y, Liu J, Betarbet R, Ping L, Yin L, Serrano GE, Beach TG, Peng J, De Jager PL, Haroutunian V, Zhang B, Gaiteri C, Bennett DA, Gearing M, Wingo TS, Wingo AP, Lah JJ, Levey AI, Seyfried NT. Large-scale deep multi-layer analysis of Alzheimer's disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat Neurosci. 2022 Feb;25(2):213-225. Epub 2022 Feb 3 PubMed.
View all comments by Julia TCWUniversity of Kansas
The proteomics analysis performed in this paper, on CSF collected as part of the DIAN study, is a tour de force. There are a number of intriguing findings. I was very interested by the finding that certain protein levels differed between the mutation carriers and non-mutation carriers before any changes in Aβ or tau biomarkers were detected. As most APP mutations are in the APP gene, not an “Aβ” gene, and presenilin proteins act on APP protein, one should wonder whether in familial AD (FAD) currently unclear changes in APP-relevant biology lie upstream of Aβ.
There are two fascinating rises and falls in proteins relevant to glucose that play out over decades. The first one occurs long before symptoms develop. I would presume this represents an attempt to compensate, and to maintain homeostasis. Compensation for what, and homeostasis for what, is a mystery. Perhaps it reflects an attempt to maintain bioenergetic homeostasis. Maybe it reflects an attempt at macromolecule synthesis. My guess is it reflects the latter.
Why this peak in glucose metabolism-related proteins goes down after it initially rises, then rises again down the line, is probably an important clue to the disease, even if it is not driving the disease in FAD.
There are other compelling questions. I’d love to know how these glucose metabolism-relevant proteins are accessing the CSF. Are they secreted? Do they just spill into the extracellular space as cells die?
There is also the question of spatial contribution. What cells are producing these proteins, or supplying them to the CSF, and what brain regions are contributing? Finally, why would mutations in presenilin genes and the APP gene cause changes to the glucose metabolism module? I’d also love to know how the ways in which this pattern of change recapitulates, or fails to recapitulate, sporadic AD.
View all comments by Russell SwerdlowArizona Alzheimer's Consortium
These extremely exciting and important studies provide new information about the time course of proteomic changes associated with predisposition to, and clinical progression of, autosomal dominant Alzheimer’s disease (ADAD) and sporadic AD. The papers illustrate the emerging value of proteomics in the study of AD and related dementias.
Johnson and colleagues capitalize on CSF samples and data from the Dominantly Inherited Alzheimer’s Network (DIAN), which continues to make pioneering contributions to unusually early detection, tracking, study, treatment, and prevention of AD, starting in cognitively unimpaired persons at virtually certain risk for the clinical onset of ADAD. The study leverages analyses by leaders in the study of AD proteomics from Emory University,
It provides new information about the sequence of proteomic changes associated with AD, starting more than 30 years before the estimated onset of symptoms, and their temporal relationships to the predisposition to amyloid plaques, to the ensuing pathophysiological changes of AD, and to the estimated ages at clinical onset. It introduces new opportunities to inform the molecular processes involved in the development of AD, the discovery of novel drugs for the treatment, secondary prevention, and primary prevention of AD, and the identification of persons who may benefit from relevant treatments at different preclinical and clinical stages of the disease.
It will be interesting to see the extent to which findings can be generalized to late-onset AD using legacy CSF samples, extended to the assessment of longitudinal trajectories, and expanded to proteomic findings in blood as we have begun to see in recent publications. Indeed, in their study, Walker and colleagues capitalized on extremely large longitudinal discovery and validation cohorts to establish the prognostic value of proteomic measures long before the onset of dementia. It will be interesting to see how blood-based biomarker measurements of AD and neurodegeneration in the cohorts can further clarify the extent to which these or other proteomic profiles could predict AD or non-AD dementias in the future.
We may have just started to see the emerging value of proteomics in the study of AD. Congratulations to everyone involved in this groundbreaking work.
View all comments by Eric M. ReimanHong Kong University of Science & Technology
A key advantage of large-scale proteomic analysis is the unbiased examination of protein changes in diverse biological processes beyond well-studied biological pathways and pathological changes in disease conditions. Emerging evidence demonstrates that complex diseases, such as Alzheimer’s disease and dementia, involve the dysregulation of multiple body systems and biological processes, and are not restricted to the most affected organ (i.e., the brain in these diseases). Therefore, large-scale proteomic analysis is a powerful tool to screen for novel disease biomarkers and can provide a clearer and more holistic picture of how such diseases impact the human body.
In this study, the authors examined the associations of 4,877 plasma proteins with 25-year dementia risk in >10,000 middle-aged adults. By performing this large-scale proteomic analysis, they identified 32 plasma proteins that are associated with dementia and involved in different biological processes, such as proteostasis, immunity, synaptic functioning, and extracellular matrix organization. Notably, some of these dementia-associated plasma proteins—particularly those enriched in pathways related to peripheral immune response and proteostasis/autophagy—start to be dysregulated in midlife, 10–20 years before dementia onset. Therefore, these proteins can be used to predict dementia risk with up to 78 percent accuracy. These findings collectively suggest that peripheral biological processes are dysregulated in the early stages of dementia and AD, and therefore encompass important biomarkers for characterizing and predicting disease outcomes.
Such early stage dysregulation of peripheral biological processes in dementia and AD is not surprising. Our previous large-scale proteomic profiling of AD plasma also revealed hundreds of AD-associated blood proteins that are involved in peripheral immune response, apoptosis, inflammation, etc.; some of these blood proteins start to be dysregulated upon disease progression when individuals still have normal cognitive functioning (Jiang et al., 2022). Moreover, certain blood proteins, such as IGFBP-2 and CLU, can be used to predict the risk of AD and the rate of cognitive decline (Sattlecker et al., 2014). Therefore, these findings together with the present study suggest that the dysregulation of peripheral biological processes, which may or may not be associated with the typical pathological changes of AD (i.e., ATN-related brain pathologies), is a key characteristic of early stage AD. Hence, identifying such blood biomarkers could advance our understanding of the progression of AD as well as facilitate the early detection and staging of the disease.
Another interesting aspect of the present study is that the authors integrated proteomic and genetic data to perform Mendelian randomization analysis of the plasma proteome. This identified SERPINA3 as a potential disease-causing factor of AD. Notably, a recent Mendelian randomization study also revealed that peripheral CD33 is causally associated with AD risk (Gu et al., 2022). Thus, the present study potentially demonstrates an alternative pathophysiological mechanism of AD; namely, that peripheral dysfunctions of cells and pathways are potential triggers of AD. Thus, future investigation into the underlying pathological mechanisms could provide insights for the development of novel therapeutic strategies for the disease.
In summary, this study is a good example of the utility of large-scale proteomic analysis for identifying biomarkers and therapeutic targets for AD. Proteomic profiling in additional cohorts from different centers and/or ethnic populations will corroborate the capability of these biomarkers to reflect or contribute to the progression of AD, which will eventually benefit diagnostic and therapeutic development for the disease.
References:
Jiang Y, Zhou X, Ip FC, Chan P, Chen Y, Lai NC, Cheung K, Lo RM, Tong EP, Wong BW, Chan AL, Mok VC, Kwok TC, Mok KY, Hardy J, Zetterberg H, Fu AK, Ip NY. Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging. Alzheimers Dement. 2021 May 25; PubMed.
Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S, Williams S, Johnston C, Killick R, Simmons A, Westman E, Hodges A, Soininen H, Kłoszewska I, Mecocci P, Tsolaki M, Vellas B, Lovestone S, the AddNeuroMed Consortium, Dobson RJ. Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 2014 Apr 24; PubMed.
Gu X, Dou M, Cao B, Jiang Z, Chen Y. Peripheral level of CD33 and Alzheimer's disease: a bidirectional two-sample Mendelian randomization study. Transl Psychiatry. 2022 Oct 3;12(1):427. PubMed.
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