One-liner
Project Ab4AD aims to identify antibodies that protect against Alzheimer’s Disease (AD) by examining immune responses to specific pathogens, with the goal of developing early diagnostic tools and preventive therapies.
Team
Principal Investigator: Dr. Daniel Bar
Project Managers: Ella McCarthy-Page, Aidin Kazemizadeh, David Falvo, Logan Bishop-Currey, Benji Leibowitz
Sourcer: Lutz Kummer
Key Researchers and Collaborators: Dr. Omer Bender, Dr. Naor Sagy
Simple Summary
TL/DR
Project Ab4AD focuses on identifying antibodies in elderly individuals without dementia that protect against Alzheimer’s Disease. By comparing these immune responses to those of AD patients, the project aims to develop diagnostic tools and therapies that can predict and prevent AD, offering new hope for millions of people and their families.
Summary
Alzheimer’s disease (AD) has been associated with infections generated by several pathogens. For example, infections with either Herpes simplex virus 1 (HSV-1), or the periodontopathic bacteria Porphyromonas gingivalis, significantly increase the risk of developing AD later in life. Indeed, these pathogens have been demonstrated in brain samples from AD patients. In mice and cellular models, these microbes are causally linked to the production of the amyloid ß (Aß) peptide, the major component of amyloid plaques. Furthermore, blocking P. gingivalis neurotoxicity by small molecules inhibiting its virulence factors reduces neuroinflammation and preserves neural cells in the hippocampus. Interestingly, though HSV-1 and P. gingivalis exposure is ubiquitous, only a portion of affected individuals will develop AD. We propose that specific immune responses modulate the risk of developing AD following exposure to pathogens. We will test this hypothesis by screening saliva and blood samples of nondemented elderly (90+) individuals (No-AD) for antibodies against AD-linked pathogens. We will expand these tests using complete human proteomics to identify auto-antibodies for brain (normal and AD) proteins. The immune response of No-AD, AD patients (AD group) and younger controls (C) to various pathogens will be compared. We will identify which pathogenic proteins are recognized by the immune system of each subject, and find statistically significant differences between the groups. This will allow us to identify antibodies that protect from AD. Based on these data, we will build a predictive model of immune-dependent AD-avoidance, and test it on archived blood samples where the clinical results are known. A successful identification of a protective immune response will (i) provide an invaluable early diagnostic tool and (ii) establish a path toward the development of a vaccine. This proposal requests $50k USD in (ETH) funding.
Problem
Alzheimer’s Disease (AD) is a neurodegenerative disease that is responsible for 60–70% of cases of dementia. It is characterized by three core pathologies: (i) accumulation of amyloid β (Aβ) plaques, (ii) accumulation of hypophosphorylated tau protein in the form of intracellular neurofibrillary tangles (NFT) and (iii) sustained inflammation (Kinney et al. 2018; Akiyama et al. 2000). These pathologies are directly linked to synaptic loss and neurodegeneration culminating in cognitive and behavioral dysfunction.
Pathogen involvement in AD
The AD “pathogen/infection hypothesis” suggests that chronic infection by viral/bacterial pathogens may be a trigger for sporadic AD onset during aging (Fulop et al. 2018; Seaks and Wilcock 2020). Substantial evidence from multiple domains support the involvement of pathogens in AD. For example, a population-based cohort using the Taiwan’s National Health Insurance Research Database (NHIRD) found that a new Herpes Simplex Virus (HSV) infection increases the risk of dementia by 2.56 fold during an 11 year followup. Interestingly, a timely treatment with anti-herpetic medications reduced the hazard ratio to 0.092 (Tzeng et al. 2018). HSV, along with other pathogens, were found in brains of AD patients and specifically in Aβ plaques (Wozniak, Mee, and Itzhaki 2009; Vigasova et al. 2021; Wainberg et al. 2021). Similarly, the immune response to these pathogens is different between AD patients and controls (Dominy et al. 2019b; Letenneur et al. 2008).
These pathogens may invade the CNS directly by translocating across the blood-brain-barrier or by secreting toxins that circulate to the brain and dysregulate its neurological functions. Pathogens may increase the risk of AD, by directly or indirectly promoting the accumulation of Aβ and NFT accumulation (Bulgart et al. 2020; Sheng et al. 2003; Kumar et al. 2016; Soscia et al. 2010). Furthermore, these pathogens, along with the accumulated Aβ and NFT, cause sustained inflammation and hypersensitive glial cells (Kinney et al. 2018) leading to direct and indirect neurotoxicity. A persistent infection is not a strict requirement, as the infection can initiate a cascade of events that result in chronic inflammation that persists after the initial infection is cleared (Sochocka, Zwolińska, and Leszek 2017). Specific pathogens have been linked to AD, and may be linked to neuroinflammation, here we will address three of them:
I. Porphyromonas gingivalis and AD: Multiple studies link periodontitis and related pathogens to AD. For example, a high titer of antibodies against peripathogenic bacteria was detected in the serum of elderly who progressed to AD (Sparks Stein et al. 2012). Periodontal inflammation, resulting from the bacterial infection of Porphyromonas gingivalis, has been associated with the accumulation of Aβ plaques in the brain (Kamer et al. 2015). One explanation for the accumulation of Aβ plaques is that Aβ functions as an antimicrobial peptide (Kumar et al. 2016; Spitzer et al. 2016; Soscia et al. 2010) and its secretion is a defense mechanism from pathogens. Orally administered P. gingivalis is able to infiltrate the mouse brain, and cause neural injury (Poole et al. 2015). Moreover, oral infection with P. gingivalis in mice expressing the human amyloid precursor protein (hAPP-J20), causes cognitive impairment (Ishida et al. 2017). P. gingivalis also infiltrates mature neurons in neuronal cultures (Haditsch et al. 2020). This infiltration results in the display of AD-like neuropathological signs such as hyperphosphorylation of tau and cytoskeletal disruption. Recently, this periopathogenic bacterium and its associated toxins (gingipains) were identified in the brains of AD patients (Dominy et al. 2019b).
II. Herpes-Simplex-1 virus and AD: Another pathogen linked to AD is the Herpes-Simplex-1 virus (HSV-1). Population-level data show that senile dementia (SD) is far more common in HSV-seropositive than in HSV-seronegative subjects, while antiviral treatment significantly decreases the odds of developing SD in the future (Itzhaki 2018). In-vitro, a 3D brain model infected
with HSV-1 mimics multiple aspects of the disease, with multicellular amyloid plaque–like formations, neuroinflammation, and decreased functionality. Importantly, these characteristics are generated in the absence of any exogenous mediators of AD (Cairns et al. 2020).
III. Chlamydia pneumoniae and AD: The intracellular bacterial pathogen Chlamydia pneumoniae has also been associated with AD. Pathogen DNA was significantly more common in AD brains than controls (Brian J. Balin et al. 2018; B. J. Balin et al. 1998). These findings were replicated by several groups, but failed to replicate in others (Brian J. Balin et al. 2018; Nochlin et al. 1999). Immunolabeling identified the pathogen in relevant brain regions, and C. pneumoniae was shown to promote neuroinflammation (Brian J. Balin et al. 2018). In some animal models, a progressive development of relevant neuropathology was observed 1–3 month-post-infection (Brian J. Balin et al. 2018).
Auto-antibodies in AD
Autoantibodies binding a variety of proteins, modified or aggregated proteins, as well as other biomolecules, have been associated with AD (Wu and Li 2016). A protective role was suggested to some of these auto-antibodies (Du et al. 2001; Qu et al. 2014), while others have a pathogenic/deleterious effect (Dinkins et al. 2015; Vacirca et al. 2012).
That these pathogens are directly related to AD is an established paradigm. However, the underlying immune mechanism leading to the development of pathologies is not yet clear. Taken together, these observations suggest that an ineffective immunological response against specific pathogens, along with brain pathogenic infection, are key steps in the etiology of AD. We do not expect a single pathogen to be dominant, and the most common pathogens may change across geographical location and even time. The resulting disease depends on the specifics and mannar of pathogen exposure, and of the patient’s genetic characteristics. These may include known risk-factors that interact with pathogens (Linard et al. 2020) as well as immune-related individual variance. Indeed, genes of immune-related tissues and immune-related pathways are highly enriched in AD Genome-Wide Association Studies (GWAS) (Jansen et al. 2019; Kunkle et al. 2019). Exposure to AD associated pathogens is very common. DNA from these pathogens are commonly found in the brains of a significant fraction of non-demented individuals (Brian J. Balin et al. 2018; Dominy et al. 2019a). Interestingly, of nine brains previously analyzed (Dominy et al. 2019b), the only brain lacking P. gingivalis DNA was that of a 102-year-old non-demented individual, the oldest subject tested. In light of the prevalence of P. gingivalis brain infiltration, we speculate that an immune response may modulate the risk of developing AD. This modulation is caused either by a complete prevention of brain colonization by P. gingivalis or by limiting the spread of P. gingivalis and neutralizing its virulence factors, reducing amyloid β peptide (Aβ1–42) production. We expand this hypothesis to testing in an unbiased way (complete proteomics) two other AD-associated pathogens: HSV-1 and C. pneumoniae. In addition, auto-antibodies have been suggested to have a major role in AD (Wu and Li 2016; Lim et al. 2019; Wang et al. 2020). We will also test whether auto-antibodies to normal or pathological human brain proteins participate in modulating this disease (for example through interactions with specific forms of Aβ1–42 and TNF). We will test this hypothesis by comparing the immune response of non-demented elderly individuals (Non-AD) to that of subjects with probable AD (AD), again utilizing complete proteomics. As the average age in the Non-AD group is higher than the AD group, we added an additional control (C) group, of nondemented individuals that age-match the AD group. We will screen saliva and blood antibodies for binding of AD-associated pathogen proteins, as well as for autoantibodies binding brain proteins. For statistically significant hits, we will explore the potential physiological relevance of identified targets (virulence factors, prime targets for neutralizing antibody, Aβ/TNF binding etc.). Finally, we will build an immune-dependent AD-avoidance model, and test its predictive power on archived blood samples.
Solution
The goal of this project is to identify antibodies distinguishing non-demented elderly individuals (Non-AD) from AD patients. To achieve this goal, we established a protocol, where we immobilize the antibodies and retrieve the respective antigens, in a hypothesis-free manner. We will identify AD-protective antibodies by comparing the antigen binding profiles of multiple Non-AD, AD and control individuals.
I. Protocol development. To gain an unbiased view of the antibody repertoire towards a target of interest, we fine-tuned a mass-spectrometry based assay (Fig. 1). First, we immobilized a broad range of antibodies (IgG, IgM, IgA, IgE and IgD), directly from a primary human sample (saliva or blood), onto protein L beads (i). Next, the immobilized antibodies were incubated with protein lysates, and bound the respective antigens (ii). Finally, bound antigens were eluted, digested, multiplexed and analyzed by mass spectrometry (iii).
In parallel, using an orthogonal method, we established that healthy controls with no history of periodontal disease have sufficiently high levels of antibodies in blood and saliva to generate a strong reaction for specific P. gingivalis proteins. For proof-of-principle, we took saliva samples from a healthy young volunteer (V1) and used it to test for IgG antibodies against P. gingivalis, as well as against Escherichia coli, serving as a positive control. Dot-blot experiments showed we can clearly detect an immune reaction from saliva, with V1 having antibodies for E. coli, and to a lesser extent to P. gingivalis (Fig. 2A). These results were replicated in a Western blot (Fig. 2B,C), where a strong reaction to multiple proteins was seen for E. coli, and several specific but weaker bands for P. gingivalis. Blood and saliva samples from additional volunteers generated similar results.
Fig. 2: Saliva antibodies identify specific bacteria and proteins. A. Dot blot ECL image of serial dilutions of P. gingivalis and E. coli (positive control), probed with saliva from V1, followed by a secondary anti-IgG HRP-conjugated antibody. B. 1D gel image of total protein stain of P. gingivalis (PG) and E. coli (EC) bacterial lysates separation. C. Image of Western blot analysis of the gel from (B), using V1 saliva as the source for primary antibodies.
II. Sample collection
This work was approved by the Ethics Committee of Tel-Aviv University (proposal no. 0000117-1) and Fliman Geriatric Hospital IRB (920200003). Saliva samples were collected from consenting patients undergoing routine dental treatments either at the school of dental medicine at Tel-Aviv University, or at one of the affiliated hospitals or clinics. Blood samples were collected from consenting patients at Fliman Geriatric Hospital. Legal guardian consent was required for demented individuals. We prioritize the collection of samples from older individuals (>90) with no signs of AD or dementia, as well as patients with probable AD (patients diagnosed as having AD by a neurologist, any age, including age matched to non-AD, if available). As the age of the Non-AD group is likely to be higher than that of the AD group, forming a potential bias, we also collected samples from nondemented controls (C) matching the age of our AD group. These will allow us to account for any age-dependent bias.
III. Proof-of-principle
The experimental protocol was extensively calibrated, to allow for maximal immobilization of antibodies, followed by efficient capture and mass-spec identification of soluble and insoluble antigens.
Samples from 4 AD and 4 Non-AD patients, incubated with P. gingivalis lysate, were multiplexed using 10 plex Tandem Mass Tags (TMT), along with negative and positive controls. We used MaxQuant to search resulting mass-spectrometry files. Samples were searched against the human, P. gingivalis proteome. As expected, identified human proteins consisted predominantly of immunoglobulins, efficiently captured by the protein L beads. Several P. gingivalis proteins were identified as having differential abundance between AD and Non-AD samples. One example is B2RKV3/DPP7, a dipeptidyl-peptidase, preferentially bound by antibodies of AD patients (Fig. 3). Other peptidases are known to be the major virulence factors of P. gingivalis (James 2008). DPP7 is present on the cell surface, but also in extracellular membrane vesicles, where it has a key role in degrading proteins to serve as the P. gingivalis carbon source. This makes it potentially a prime target for antibodies. We note that in these proof-of-principle experiments, we are severely underpowered to gain p-values that will pass the required multiple-test correction.
Opportunity
Unmet Clinical Need:
Current treatments for AD focus on managing symptoms rather than addressing the underlying causes. Medications available today can temporarily alleviate cognitive and behavioral symptoms, but they do not stop or significantly slow the progression of the disease. There is a critical need for new therapies that target the root causes of AD to provide more effective and lasting benefits. Our research aims to explore the role of the immune system and pathogens in AD, with the goal of developing better diagnostic tools and treatments to improve outcomes for those affected by this condition.
Potential Impact
Our research intends to develop diagnostic tools and, eventually, therapies targeting the immune response against AD. If early signs of AD are detected way before the symptoms begin, the risk of developing AD would be significantly reduced by simply using safe and standard antivirals at a local Pharmacy or GP.
Our Vision and Long-Term Objective:
Early Detection and Intervention: We aim to identify specific antibodies in saliva and blood that either protect against or contribute to Alzheimer’s. This will enable the development of non-invasive diagnostic tools that can detect AD risk years before symptoms appear, allowing for early and effective intervention.
Preventive and Therapeutic Solutions: Once our diagnostic tools have aggregated enough data and by pinpointing these protective antibodies, we can potentially develop vaccines or treatments that enhance the body’s natural defenses against Alzheimer’s. This proactive intervention could significantly reduce the incidence of AD, saving the lives of those at risk.
Innovative Diagnostic Tools: Developing early diagnostic methods based on immune profiles could revolutionize AD detection, making it more accessible and less invasive than current techniques like PET scans or cerebrospinal fluid analysis. We have already established a causal link between certain pathogens and AD development in the lab and this project paves the way for validation of our most recent findings making us closer to early AD prevention and vaccination.
Predictive Models: Creating predictive models using immune response data will allow us to identify individuals at risk for AD. For instance, untreated HSV-1 can triple dementia risk over 15 years; however, safe and standard antivirals significantly lower this risk. A new diagnostic tool can detect immune responses to HSV-1 and other germs, identifying at-risk asymptomatic individuals for early intervention, potentially preventing Alzheimer’s development.
Therapeutic Development: Current AD treatments often fall short, primarily focusing on symptom management rather than prevention or cure. By understanding the immune responses involved, we can develop therapies that target the disease at its root, offering more effective and long-lasting solutions.
Expected Outcomes:
- Identification of Protective Antibodies: Discover antibodies in non-demented elderly individuals associated with protection against AD.
- Development of Predictive Models: Create and validate models that predict AD risk using immune profiles from blood samples.
- Creation of Early Diagnostic Tools: Develop non-invasive diagnostic tools for early detection of AD through saliva and blood tests.
- Therapeutic Development: Use data from diagnostic tools for early intervention with antiviral drugs, and to develop targeted therapies such as vaccination for preventing or mitigating AD.
Relevance to Longevity
This project targets the immune system’s role in Alzheimer’s Disease (AD), aiming to uncover how immune responses and pathogen exposures influence AD development. By addressing key hallmarks of aging such as altered intercellular communication, our research seeks to mitigate the detrimental effects of chronic inflammation and persistent infections on brain health, as well as understand why certain individuals are protected against dementia despite having chronic infection. Identifying protective antibodies and understanding immune mechanisms can lead to early diagnostics, preventive vaccines, and targeted therapies, ultimately extending both healthspan and lifespan by understanding the mechanisms by which individuals are protected against some underlying processes of aging.
IP Roadmap
- Kit for identification of individual risk of AD. By identifying the antigens that indicate risk/protection from AD, a simple kit can be developed, testing the presence of such antibodies and predicting AD risk decades ahead of diagnosis. While there is currently no cure for AD, there are treatments and activities that are known to delay its onset. Aim 4 will generate the data needed to prove the sensitivity and specificity of such a kit. This path is relatively simple from a regulatory standpoint.
- Antibodies for AD treatment. Antibodies are being used for AD treatment. Our screen may identify novel and superior targets for future antibodies. These may be used for de-novo generation of antibodies and antibody-like proteins. It may also include isolation and cloning of naturally occurring antibodies, including the isolation of antigen-specific B cells from nondemented centenarians. This path is similar in cost and complexity to novel antibody-based drug development.
- Ideal targets for AD vaccine. The identified antigens will be prime targets for a vaccine, mounting a potent immune response upon contact, reducing the risk of brain colonization by pathogens, limiting their spread and reducing chronic neuroinflammation. This path is somewhat challenging, as the ideal age for vaccination is decades before symptoms appear. Regulators will need to approve the use of early biomarkers to make clinical trial time reasonable. However, such vaccines may be suitable for protection against periodontitis (P. gingivalis) or encephalitis and keratitis (HSV) in high-risk populations, allowing for an easier regulatory path.
Experimental Plan
We will identify a differential immune response to AD-associated pathogens between AD, Non-AD and C individuals (aim 1), identify a differential immune response to human brain proteins (aim 2) and validate our findings using independent methods (aim 3). A successful completion of these aims would support the hypothesis that a specific immune response modulates the risk of developing AD. It will also enable a testable, quick and cost-effective way to validate these findings on different cohorts, both by us and by others.
| Phase | Item | Description and rationale | Performed by | Desired outcome and milestone |
|---|---|---|---|---|
| 1a | Validation of immune response in AD vs Non-AD using Mass Spectrometry | Mass Spectrometry is a technique to measure proteins. Blood and saliva samples will be analysed using this approach to see whether there is a significant difference between the pathogenic proteins or AD-protective proteins (i.e. protective antibodies ) recognized by antibodies of AD and non-AD participants. | Study will be performed in house with available materials + materials that will be purchased | To identify if there is AD-associated pathogenic proteins identified by saliva and blood antibody samples. Ideally, there will be pathogenic proteins (epitopes) differentially identified between AD vs non-AD samples |
| 2a | Validation of identified targets using highly sensitive methods | VirScan is an advanced test that quickly checks a person’s blood to find out which viruses they have been exposed to by identifying specific parts of viruses targeted by the immune system. VirScan will be the most crucial data unlock provided by this funding | Study will be performed by service provider | Further validation if HSV-1 epitopes and antibodies are present in AD samples vs non-AD |
| 2b | Go/no-go 1 | Assess data from phases 1a and 2a and compare against criteria and desired outcomes and decide whether to pursue further validation | Decision will be made in collaboration between research team, sponsor, and will include community input. External advisors may be required. | Data package is sufficient to provide a decision about which hit compound(s) to prioritize |
| 3a | Further Validation with Peptide Microarrays and ELISA | Peptide microarrays are specialized devices used to analyze protein interactions and identify antibody targets. This technique will be used along with ELISA - standard assay to identify and quantify proteins - to identify specific pathogenic proteins present in AD vs non AD samples | Study will be performed in house with available materials + materials that will be purchased. quotes need to be received. | AD-associated pathogen epitopes and antibodies would be further validated using peptide microarrays. Develop coding system for anonymizing epitopes. Assess patent landscape and feasibility of synthesis of compounds |
Budget and Costs
| Item | Amount (local currency) | Amount (USD) |
|---|---|---|
| Personnel costs | 182,500 ILS | $50,000 |
| Consumables and reagents | 123,900 ILS | $34,000 |
| External consultants and partnerships e.g. chemistry, consulting, etc | 255,150 ILS | $70,000 |
| University overhead costs (35%) | 107,200 ILS | $29,400 |
| Total | 668,500 ILS | $183,400 |
Financing and VitaDAO Funding Terms
This proposal is to provide the project $50k, ~27% of the $183,400k needed to complete the milestones. The remaining funding will come from other DAOs, and individuals in the DeSci community. Funding will be open and welcome to all via Molecule’s platform: Catalyst. The project. Tokenomics will be split such that 95% of the IP Tokens will go to the funders, and 5% will go to a liquidity pool.
The licensing terms are not yet finalized. We are currently in conversations with the relevant technology transfer office (TTO), and are looking to secure funding prior to negotiating the terms. If an agreement is not reached, and the funders do not approve of the terms of the IP, the funding will be returned to both VitaDAO and individual funders.
Budget
This proposal, if passed, will allocate $50k (in ETH) of the $100k allocated to Catalyst projects as part of VDP-147. Again, if the terms are not agreed upon by the funders, then the ETH will be returned to VitaDAO.
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Senior Review Digest - Quantitative
The project was reviewed by three reviewers: a scientist, an entrepreneur, and a VC.
Below is the average scores from the reviewers out of 5 per category.
Average Scores
- Team Expertise: 3.0
- Feasibility & Data: 2.7
- Commercial Potential & Impact: 3.7
- Novelty & Market Advantage: 2.7
- IP Defensibility: 2.3
- Relevance to Longevity: 5.0
- Deal Terms: N/A (the terms have not yet been set)
- General Conviction Score 3.0 (for reference, the average score of past funded projects is 3.7)
The majority of the reviewers think that this project is a moonshot.
Two reviewers recommended that the project should be followed-up with the applicant for more information, one that it should be denied outright giving constructive feedback.
Senior Review Digest - Qualitative
Each reviewer was asked whether they would endorse the project, as well as the pros and cons they see: below are their answers.
Reviewer 1
Infection and AD are not new, unsure of the novelty. A diagnostic or treatment is decades away, no data to support they have a drug/diagnostic. Team lacks any BD/Development experience.
Pros
- Limited diagnostics and treatments for AD.
- Of course a new Dx or Tx would be beneficial.
Cons
- N/A
Reviewer 2
I would endorse the project because of its strong potential to have an impact on longevity and AD. However, I would recommend having a comprehensive scientific and market analysis of the current status of work being done related to this approach. Also how the involvement of the VitaDAO community in the project should be determined.
Pros
- Looking into prevention therapy rather than treatment strategies for AD and other neurodegenerative diseases.
- The team is experienced in scientific research with some preliminary data.
- The study will add positively to the current knowledge base on AD and pathogen relationships, which could lead to new discoveries / potential therapeutic strategies for AD.
Cons
- The project is currently in the very early stages and the current market analysis is not clear.
- The team might need advisors/partnerships to take it to the next level.
- It’s a moonshot project with a high-risk high-reward scenario.
Reviewer 3
I am always eyeing on AD related diagnosis & treatment. I am seeing deals that have core technology associated with biosensors to detect pathogens & antigens in blood, saliva & sweats. There might be synergy between the companies and I am more than happy to introduce to the team. Finding the right antigens and antibodies associated with development of AZ is crucial at the stage, if the 50K is used for doing this exclusively, I am happy to support the project publicly.
Pros
- Early detection & vaccine for AD is promising if both can be developed.
- The team has some expertise in finding the pathogen related antigens/antibodies.
Cons
- The strategy of developing an early diagnosis tool as an important milestone.
- Lacking vaccine experts for the moonshot plan.
- Lacking IP protection for diagnosis tool.
- Agree
- Revisions Requested [Details in Comments]
- Disagree