Tear fluid reflects the altered protein expressions of Alzheimer's disease patients in proteins involved in protein repair and clearance system or the regulation of cytoskeleton

Abstract

Background

New biomarkers that improve diagnosis of Alzheimer's disease (AD) are warranted. Tear fluid (TF) containing variety of proteins that reflect pathophysiological changes of systemic diseases makes TF proteins potential biomarker candidates for AD.

Objective

We investigated the expression levels of TF proteins in persons with mild AD and cognitively healthy controls (CO) to find out if altered proteins may link to the AD pathophysiology.

Methods

We analyzed the data of the 53 study participants (34 COs, mean age 71 and Mini-Mental State Examination (MMSE) 28.9 ± 1.4 and 19 persons with AD, CDR 0.5–1, mean age 71 and MMSE 23.8 ± 2.8). All went through neurological status examination, cognitive tests, and ophthalmological examination. TF was collected using Schirmer strips. The TF protein content was evaluated via mass spectrometry-based proteomics and label-free quantification.

Results

Eleven proteins having a role either in protein repair and clearance system, or regulation of cytoskeleton, showed altered expression in AD group compared to CO group. Seven of them were significantly (p ≤ 0.05) upregulated (Sti1, Twf1, Myl6, Otub1, Pls1 and Caza1) or, downregulated (HSP90) in AD group.

Conclusions

Altered expression of all these up- or downregulated proteins may be linked to AD pathophysiology. Thus, our results are encouraging for searching new biomarker candidates for AD. TF is potential biomarker candidate, because TF seems to reflect altered protein levels already in mild AD dementia.

Keywords

Alzheimer's disease chaperones cytoskeleton pathophysiology proteomics tear fluid

Introduction

Globally, Alzheimer's disease (AD) is the most common cause of memory disorders. It will eventually lead to increased need for help in everyday activities, loss of independency, increased cost for care, and social burden for families.¹ The pathophysiology of AD remains unclear and is under extensive research. Also, early diagnosis of AD is challenging. The biomarkers that improve diagnostic accuracy are warranted. Easily accessible body fluids, such as tear fluid (TF) that reflect aging and pathophysiological changes of systemic disease, like AD are interesting biomarker sources.^2–6

TF contains over 1500 different measurable proteins, and it is a complex mixture of proteins such as enzymes, neuropeptides, and protective proteins.⁷ The composition of TF proteins is not constant and for example, aging, several eye diseases as well as in neurodegenerative diseases are showed to change TF protein expressions.^2,3,7

Many TF proteins, like lipocalin-1, lysozyme-C, and prolactin inducible proteins (PIP) are produced by the lacrimal gland or can be first secreted to serum from other sources, and then reaching the eyes via conjunctival capillaries. Such proteins are for example many inflammatory mediators.⁷ Because proteins are secreted into TF in many ways, the proteomics study might offer us a novel AD biomarker candidate and may also help us to understand underlying pathophysiology of AD.^5,8

There are only few studies published earlier concerning TF protein changes in AD. In 2016 Kallo et al. published a pilot study of 9 person with AD and 14 controls and compared their TF protein composition. They found that a combination of four selected proteins lipocalin-1, dermcidin, lysozyme-C and lacritin may be a promising biomarker for AD. They also showed that proteins produced by lacrimal glands were significantly downregulated in persons with AD and speculated that AD might lead to lacrimal gland dysfunction. In addition, they reported that the rate of tear flow was significantly increased in AD.⁸

In 2019 Kenny et al.l. showed that TF proteins differ in healthy controls (COs) compared to persons with AD. The persons with AD seem to have a unique proteomic and microRNA composition in TF sample, as they found 12 proteins that were differently expressed in persons with AD than COs. These 12 proteins are involved in various cellular processes: they regulate endopeptidase activity, protein folding, cellular amino acid metabolic process and mRNA stability. However, this study did not show increased TF flow or total protein concentration.² Furthermore, 2021 Gijs et al. found out that tau and amyloid levels in TF reflects the severity of AD.⁴

In AD, the underlying pathophysiology starts to develop several years before clinically diagnosed AD and there is emerging evidence of the role of inflammation in AD pathophysiology.¹ The increased oxidative stress (OS) is a well- recognized contributor in aging but is also involved in many neurodegenerative disorders. Mitochondrial OS and disturbed energy metabolism might be the first sign of AD in the brain.⁹

An imbalance between generation and suppression of reactive oxygen species (ROS) will lead to an undesirable result such as protein unfolding and damage, which in turn changes the protein structure and leads to detrimental protein aggregation.^9,10 Heat shock proteins (HSPs) have a key role to repair protein damage, but under chronic OS their capacity may be exceeded that coincides with disturbed proteostasis including proteasomal and autophagy clearance.¹¹ There is an emerging proof that protein aggregates regulate microglia activation and their dysfunction associates with AD pathophysiology.^11,12 Currently, there is evidence that protein aggregation is typical hallmark in several neurodegenerative diseases.¹³ Our aim was to examine if TF protein analysis distinguishes persons with AD from COs and whether up- or downregulated TF proteins reflect underlying AD pathology.

Methods

Ethics statement

The study adhered to the principles of the Declaration of Helsinki and has been evaluated and accepted by The Research Ethics Committee of the Northern Savo Hospital District (Dnro: 482/2017). All study participants were verbally informed, read the information letter, and signed the informed consent prior to participation. The proxy consent was signed for individuals diagnosed with dementia stage AD.

Study design and study protocol

We recruited a total of 53 volunteers aged >60 years from the Brain Research Unit of the University of Eastern Finland and from the memory clinic of Kuopio University Hospital NeuroCenter for this cross-sectional study. Of them, 19 persons had been diagnosed with AD before enrolling into this study (AD group) and 34 persons were classified as a cognitively healthy persons, which we used as a control group (CO group). The AD diagnoses were made by a geriatrician or a neurologist in the memory clinics. Diagnoses were made on the bases of the revised National Institute on Aging and Alzheimer's Association (NIA/AA) criteria.¹⁴ Brain MRI (1.5 T or 3 T MRI scanner) or CT, comprehensive cognitive testing (Consortium to Establish a Registry for Alzheimer's Disease [CERAD] neuropsychological battery or neuropsychological tests), and differential diagnostic laboratory tests were carried out before diagnosing AD to rule out other etiologies of memory decline. The exclusion criteria for this study were memory decline due to other etiologies than AD, limited co-operation because of moderate to severe AD, diabetes, and eye diseases such as glaucoma, dry eyes, or age-related macular degeneration.

At the first appointment a vast amount of demographic information was obtained from the participants including medical history, medication, and family history for AD (Table 1). The participants with AD family members were also interviewed, and Clinical Dementia Rating (CDR) scale was determined for all participants.¹⁵ Thorough neurological and cardiorespiratory examination was carried out by a neurologist or the study doctor. Participants with any signs of upper motor neuron defects, parkinsonism, balance abnormalities, or ataxia were excluded. The Finnish version of the CERAD neuropsychological battery, including the Mini-Mental State Examination (MMSE), was carried out for all participants.¹⁶ In the CO group the CERAD test results were normal in all participants and there was no decline in daily activities based on demographic and CDR interviews (CDR 0).

Table 1.

Demographic data of control and Alzheimer's disease (AD) study groups.

	CO N = 34	AD N = 19	p (CO-AD)
Age, y (±SD)	71 (5.3)	71 (6.8)	0.817
Gender, female N (%)	19 (56)	12 (63)	0.413
MMSE result (max 30) (±SD)	28.6 (1.4)	23.9 (2.8)	0 . 000
MMSE range	25–30	16–27	NA
Family history N (%)	19 (57.6)	12 (63.2)	0.462
BMI (kg/m²) (±SD)	25.3 (2.6)	24.5 (3.4)	0.338
Coronary artery disease N (%)	3 (8.8)	2 (10.5)	0.596
Hypercholesterolemia medication N (%)	14 (41.2)	5 (26.3)	0.218
Hypertension medication N (%)	19 (55.9)	12 (63.2)	0.413
APOE allele 4 carriers N (%)	15 (44.1)	14 (82.4)	0 . 009

Values are presented as Number of participants (N), means ± Standard deviation (SD) or as percentages (%). Number of APOE allele 4 carriers is given as number of one or two alleles carrier (N, %). CO: healthy control group; AD: Alzheimer’s disease group; p: p-value (≤0.05) indicate significant differences between groups; SD: standard deviation, %: percentages; MMSE: Mini-Mental State Examination, range 0–30; NA: not applicable; BMI: body mass index given as kg/m²; APOE: apolipoprotein E. p-value ≤0.05 indicate significant differences (marked in bold).

APOE genotyping

Genomic DNA was extracted from venous blood samples using the QIAamp DNA blood mini extraction kit (QIAGEN). APOE gene alleles were determined using TaqMan genotyping assays (Applied Biosystems (ABI), Foster City, CA, USA) for two single nucleotide polymorphisms (rs429358 and rs7412) and an allelic discrimination method on the ABI 7000 platform.¹⁷

Statistical analysis

Statistical analyses were performed in SPSS 22 software (SPSS Inc Chicago, IL, USA). p < 0.05 indicated a significant difference. Demographic data, CERAD test (MMSE) and ocular data (Table 2) results are presented as mean ± standard deviation (SD) or as number of cases and proportions (%). We used t-test and one-way analysis of variance (ANOVA) followed by LSD post-hoc test. Group differences were expressed as mean difference and 95% confidence intervals.

Table 2.

Summarized ocular data, collected from healthy control (CO) and Alzheimer’s disease (AD) study groups.

Ocular data	CO n = 36	AD n = 20	P (CO-AD)	Mean difference/(95% CI)
Schirmer mm in 5 min, right eye	9.28 (6.9)	8.3 (8.2)	0.819	0.96 (−3.53–5.45)
Schirmer mm in 5 min, left eye	9.03 (6.1)	11.2 (8.3)	0.157	−2.13 (−6.52–2.26)
TBUT, right eye	5.54 (3.1)	5 (3.8)	0.598	0.54 (−1.56–2.64)
TBUT, left eye	5.6 (3.2)	5 (3.8)	0.678	0.6 (−1.51–2.71)
Conjuctival redness, right eye			0 . 025
0 = no redness % (n)	0	0
1 = mild redness % (n)	58.3 (21)	85 (17)
2 = moderate redness % (n)	41.7 (15)	10.0 (2)
3 = sever redness % (n)	0	5 (1)
Conjuctival redness, left eye			0.062
0 = no redness % (n)	0	0
1 = mild redness % (n)	58.3 (21)	80.0 (16)
2 = moderate redness % (n)	41.7 (15)	15.0 (3)
3 = sever redness % (n)	0	5 (1)
Lid margin redness, right eye			0.162
0 = no redness % (n)	0	0
1 = mild redness % (n)	69.4 (25)	45.0 (9)
2 = moderate redness % (n)	25.0 (9)	50.0 (10)
3 = sever redness % (n)	5.6 (2)	5.0 (1)
Lid margin redness, left eye			0.266
0 = no redness % (n)	2.8 (1)	0
1 = mild redness % (n)	66.7 (24)	45.0 (9)
2 = moderate redness % (n)	25.0 (9)	50 (10)
3 = sever redness % (n)	5.6 (2)	5 (1)
Conjuctival fluorescein staining, right eye			0.108
0 = absent/very slight % (n)	5.6 (2)	25 (5)
1 = mild % (n)	80.6 (29)	65 (13)
2 = moderate % (n)	13.9 (5)	10 (2)
Conjuctival fluorescein staining, left eye			0.207
0 = absent/very slight % (n)	5.6 (2)	20 (4)
1 = mild % (n)	77.8 (28)	60 (12)
2 = moderate % (n)	16.7 (6)	20 (4)
Cornea fluorescein staining, right eye			0.503
0 = absent/very slight % (n)	44.4 (16)	60 (12)
1 = mild % (n)	38.9 (14)	25 (5)
2 = moderate % (n)	16.7 (6)	15 (3)
Cornea fluorescein staining, left eye			0.656
0 = absent/very slight % (n)	47.2 (17)	60 (12)
1 = mild % (n)	33.3 (12)	25 (5)
2 = moderate % (n)	19.4 (7)	15 (3)

Values are presented as means ± Standard deviation (SD) or as percentages (%). CO: healthy control group; AD: Alzheimer's disease group; n: number of cases; p: p-value (≤0.05) indicate significant differences between groups; mmHg: millimeters of mercury; mm: millimeter; TBUT: tear break up time. p-value ≤0.05 indicate significant differences (marked in bold).

Eye examination

Slit-lamp examination was done by a CSO (Costruzuzione Strumenti Oftalmici, Firenze, Italy) biomicroscopy. TF stability was observed by TF Break-up Time (tBUT) in biomicroscopy and Schirmer test strips were used to measure TF production.^18,19 Lid margin and bulbar conjunctival redness were scored by using Institute of Eye Research (IER) grading scales (very slight-0, mild-1, moderate-2, severe-3).¹⁸ Corneal and conjunctival fluorescein staining patterns were scored with Oxford grading scale, form 1(absent) to 5 (marked).¹⁹

Tear collection and sample preparation

TF samples were collected by using Schirmer strips (Tear Touch; Madhu Instruments) without anesthesia. The strips were placed under the lower eyelid for 5 min, and after removal, the strips were placed in to 1.5 mL Eppendorf tubes and stored in −80°C to await further processing.

In-solution protein digestion

The proteins from the Schirmer strips were eluted with 200 µl PBS. Next, the proteins were precipitated with cold acetone. Subsequently, in-solution digestion of the proteins with 2 µg trypsin GOLD (Promega, Madison, WI, USA) after reductive alkylation using DTT and iodoacetamide was performed.

Proteins identification and label-free quantification

The tryptic peptides were purified using 10 µl OMIX-C18 micro-SPE pipette tips (Agilent, Santa Clara, CA, USA) and injected to the LC-MS system consisting of a timsTOF Pro (Bruker Daltonik, Bremen, Germany) which was coupled online to a nanoElute nanoflow liquid chromatography system (Bruker Daltonik, Bremen, Germany) via a CaptiveSpray nanoelectrospray ion source. The LC/MS data were searched against the human Uniprot database (20,431 entries), with PEAKS X + software version 10.5 (Bioinformatics Solutions, Waterloo, ON, Canada). A false-discovery rate of 1% was applied to the datasets.

For label-free quantification (LFQ) using PEAKS, ID directed LFQ with outlier removal was applied. The following parameters were used on peptide features: quality ≥ 4, peptide ID count per group ≥ 1, detected in at least one sample per group. The following parameters were applied on protein: FDR ≤ 5%, fold change ≥ 2, and significance method ANOVA with at least 2 peptides. The significance score is calculated as the −10log10 of the significance testing p-value.

Results

Demographic data

The demographic data of the participants are presented in (Table 1). MMSE results and the number of APOE allele 4 carriers differed significantly between the study groups. In the CO group mean MMSE was 28.6 (±1.4) and in the AD group 23.9 (±2.8) (p < 0.000). The proportion of APOE allele 4 carriers was 44.1% in the CO group and 82.4% in AD the group (p < 0.009).

Ocular data

The production of TF, its stability, lid margin redness, conjunctival redness or conjunctival and cornea fluorescein stainings were not significantly changed between study groups. The data are presented in (Table 2).

Proteomics data

We found in total 47 interesting proteins which were up- or downregulated in AD compared to CO group and categorized them in the groups based on the function of the protein. In this study, we report total 11 proteins which are related to protein repair and clearance or regulation of the cytoskeleton. The decrease or increase of protein (ratio) contents in AD samples were compared to the CO samples (normal = 1). The expression of proteins was significantly (p ≤ 0.05) either decreased, with a ratio <0.5, or increased when the ratio > 2.0 in AD group (Table 3). Significantly up-regulated proteins were F-actin-capping protein subunit alpha (Caza1), Myosin light polypeptide (Myl6), Plastin-1 (Pls1), and Twinfillin-1 (Twf1) which are involved in stabilizing the cytoskeleton of the cell. Furthermore, significantly upregulated Stress induced phosphoprotein 1 (Sti1), Ubiquitin thioesterase (Otub1), and downregulated Heat shock protein 90 (Hsp90) were identified, which all have a role in protein repair and clearance system in the cells.

Table 3.

Up- and downregulated tear fluid (TF) proteins in person with Alzheimer's disease (AD) compared to healthy controls (CO) are classified with their ratio values.

Protein name	Function in cell	Short name	Gene name	Ratio (AD:CO)	Significance score
Upregulated protein
Stress induced phosphoprotein 1	repair & clearance	Sti1, Stip1	STIP1	3.17	35
Twinfillin-1	cytoskeleton	Twf1	TWF1	2.66	63
Myosin light polypeptide	cytoskeleton	Myl6	MYL6	2.4	45
Ubiquitin thioesterase	repair & clearance	Otub1	OTUB1	2.39	41
Plastin-1	cytoskeleton	Pls1	PLS1	2.37	42
F-actin-capping protein subunit alpha	cytoskeleton	Caza1, Capza1	CAPZA1	2.09	81
Downregulated protein
Heat shock protein HSP90	repair & clearance	Hsp90	HSP-90	0.46	31
NSFL1 cofactor p47	repair & clearance	Nsfl1	NSF1C	0.55	43
Heat shock protein HSP70	repair & clearance	Hsp70	HSP-70	0.58	39
Platelet-activating factor acetyl hydrolase IB subunit alpha	repair & clearance	Pafah1B1	PAFAH1B1	0.65	105
Tropomyosin alpha-4 chain	cytoskeleton	Tpm4	TPM4	0.66	90

CO value is set up as value one (Ratio). The differences between AD and CO are significant (p < 0.05) if ratio value is <0.5 or >2.0. ANOVA was applied as significance method with at least two used peptides. The significance score is calculated as the −10log10 of the significance testing p-value.

Discussion

AD is a complex disease involving a wide variety of changes in proteins and processes in cellular levels. TF proteins seems to reflect pathophysiological changes of many systemic diseases, like AD and thus is a potential new biomarker candidate.^2–6 In our proteomics study, we found 11 TF proteins with altered expressions in AD group. According to the literature, these proteins seem to have important roles in protein repair and clearance system or stabilizing cytoskeleton of neuronal or other type of cells, and thus may be linked to the pathophysiology of AD or dementing process.

The study populations were homogenous regarding the demographics or eye data. The production of TF was not changed in participants with AD, which supported the Kenny et al. 2019 results. Only the AD-linked variables, the number of APOE4 carriers and MMSE results varied between the groups as expected. Participants with AD carried APOE4 alleles more often than the controls and their performances of MMSE was poorer. All participants with AD were in the mild stage of the disease (CDR 0.5–1.0). Individuals with eye disorders and diabetes were excluded, which allowed us to examine AD-related changes in TF proteins.

One of the main characteristics in AD brain is progressive neuronal loss in the hippocampus and cerebral cortex.²⁰ Misfolded and aggregating tau-proteins, and accumulation of Aβ peptides to the plaques are important etiologies behind neuronal dysfunction and degeneration in AD. Accumulation of harmful proteins in AD brain indicate disruptions of the cell repair system. To maintain the cellular homeostasis, the function of chaperone machinery has a crucial role. Heat shock proteins, Hsp70 and Hsp90 are chaperone proteins, which together with their co-chaperones, like Stress induced phosphoprotein 1 (Sti1), are first involved in misfolded protein refolding.^13,21 Once their repair capacity is exceeded to avoid harmful effect in cells, misfolded and aggregated proteins, such as tau, needs to be targeted to proteasomal degradation pathway. Recently, it has been reported that Hsp70 and Hsp90 form a multichaperone complex, which have together high affinity to aggregated tau, and thus are important regulators for aggregated tau protein.²¹ Sti1 is an important co-chaperone and it’s role is to facilitate substrate transfer between Hsp70 and Hsp90.^13,22 Revealing disturbed stress response, we observed that Hsp70 and Hsp90 levels were decreased, while Sti1 was upregulated in TF isolated from participants with AD. One can consider that Sti1 response is a compensatory mechanism against Hsps.

Ubiquitination is a common post-translational modification and an important part of protein degradation when the misfolded and aggregated protein must be deleted by the proteasomal degradation. One or more ubiquitin molecules attach to a protein and, these ubiquitin's determine the fate of the protein. Finally, ubiquitin-tagged protein will be degraded. Most of intracellular misfolded proteins degrade via this way.²³ The ubiquitin-proteasome system (UPS) is a multicomponent cellular system including several enzymes; ubiquitin activating enzymes (E1), ubiquitin conjugating enzymes (E2), ubiquitin ligase enzymes (E3), deubiquitinating enzymes (DUB) and 26S proteasome.^23,24–28

Dysfunction of the UPS has been associated to aggregation of misfolded proteins in AD and the damages of UPS in several brain region of AD brain is reported. Seven E3 ligases, like platelet-activating factor acetyl hydrolase IB subunit alpha (Pafah1B1), which is in line with our TF observation, are reported to be downregulated in several brain regions.²⁴ The role of UPS ligases is either to activate, transfer, or covalently link ubiquitin to a substrate protein^23,27 and on the contrary DUB enzymes can remove ubiquitin from the substrate protein.^23,28 Ubiquitin thioesterase (Otub1) belongs to DUB enzymes, it has reported many roles in cells, for example it controls apoptosis of neuronal cells and accumulation of tau by ubiquitination.²⁹ Nsfl1 cofactor p47 are also involved in proteasomal degradation by exporting the misfolded protein in the cytosol and the proteasomal degradation.³⁰ Decreased Nsfl1 reveals disturbed proteasomal clearance in participants with AD that may finally lead to cell death regulated by Otub1, which was overexpressed in our study.

It can be assumed that downregulation of chaperones and altered expressions of UPS proteins together are influencing on the aggregation and accumulation of AD related proteins to the cells, even in the case that protein expression has not altered significantly.

In AD the alterations in cytoskeleton of neuronal cells are reported to occur.²⁰ Here we reported downregulation of tropomyosin alpha-4 chain (Tpm4), and upregulation of twinfillin-1 (Twf1), F-actin-capping protein subunit alpha (CAZA1), myosin light polypeptide (Myl6) and plastin-1 (PLS1) in AD. All these proteins can be linked to the maintaining of the cell cytoskeleton by regulating actin filaments.^31–33 The neuronal cytoskeleton is forming microtubules, actin filaments, and neurofilaments. All those parts are important for maintaining of normal neurons structure and functions, like synaptic plasticity.^20,29,32

Tpm4 is expressed in postsynaptic regions, growth cones of neurons, and formation areas of neurites. The function of synapse is dependent on actin filaments and thus actin binding regulator proteins are important.³² Twf1 is an actin-depolymerizing factor, which regulate actin dynamics. Dynamic remodeling of the actin cytoskeleton is necessary for development of the dendritic spine, spine density and plasticity.^33,34 The changes in the morphology or density of dendritic spines leads to altered synaptic transmission.^34,35 CAZA1 is a heterodimer F-capping protein, which is known to regulate the growth of the barbed end of the actin filament capping activity, and it is also thought to be important for dynamics of the cytoskeleton.^36,37

PLS1 belongs to actin-bundling proteins, which are shown to be expressed for example in stereocilia in the inner ear epithelium of the hair cells.^38,39 Stereocilia are actin-based protrusions that detect sound, gravity, and head movement.³⁹ To our knowledge PLS1 is not detected in neurons in AD or not reported, but hearing loss is considered one of the risk factors for AD.⁴⁰ This may partly explain why PLS1 expression was increased in the AD group.

Myl6 is myosin alkali light chain protein and component of myosin. Myl6 is associated with disulfidptosis, which is one type of cell death mechanism. Disulfidptosis is triggered under glucose-deprived state when the high amount of disulfide molecules is accumulated to the cells, which then acts with actin cytoskeleton proteins leading to increase in disulfide bond levels within actin filaments. This cascade leads to filament contractions, collapse of cellular skeleton structure, and further cell death.⁴¹

Based on literature, we found five proteins, which seem to be important regulators of cell cytoskeleton, function of synapses and cell death, which all may be linked somehow in AD pathology.^31–41 Decreased Tpm4 expressions may indicate poorly functioning synapses, increased Twf1 altered synaptic transmission, and increased Caza1 may leads to disrupted cytoskeleton. Overexpression of Myl6 may explain high cell death and Pls1 stereocilia abnormalities in AD group.

Our findings are important considering that our participants with AD are in milder stages of AD (CDR 0.5–1). These findings supported the theory that several pathological changes started to occur in early state of the disease course. And, most likely these changes will become more evident as AD progresses and pathophysiological mechanisms accelerate consequently. It is important to understand that even if some of the protein levels did not alter statistically significantly all the changes together influences on function of cells and are important clinically.

This cross-sectional study was designed to be a pilot study to find out whether proteomic analysis reveals the differences between CO and AD in their protein expression and whether TF potential source candidate for biomarker molecules. We found several up- or downregulated proteins, which may be related to the pathophysiological changes of AD or dementia. Our study proves that TF is potential source for novel biomarkers. Interesting future research questions are when the TF proteins change during the AD progression since already in the mild AD dementia expression of several proteins are already changed.

Conclusion

This study provides new information about the cellular protein alterations related to mild AD or dementia and encourages us to continue the study to develop new easy-to-collect and non-invasive AD TF biomarkers. In addition, new findings of proteins which may be involved in the pathophysiological changes during the AD or dementia add knowledge about the disease processes.

Footnotes

Acknowledgements

We are deeply grateful to all the study participants. We also thank BEGAD research project study nurses Ulla Vanhanen, Kati Mönttinen, Marita Parviainen and Kristiina Holopainen, research leader Merja Hallikainen and statistics Tuomas Selander. This study project was supported by Juho and Lempi Pitkänen Foundation, Finland, and Finnish government research funding (VTR) the Academy of Finland Grants (333302, GeneCellNano Flagship; 339767), Päivikki and Sakari Sohlberg Foundation, Sigrid Juselius Foundation, and the Finnish Eye Foundation. Mass spectrometry-based proteomic analyses were performed by the Proteomics Core Facility, Department of Biosciences, University of Oslo. This facility is a member of the National Network of Advanced Proteomics Infrastructure (NAPI), which is funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910).

ORCID iD

Virve Kärkkäinen

Author contribution(s)

Virve Kärkkäinen: Data curation; Funding acquisition; Project administration; Supervision; Validation; Visualization; Writing – original draft.

Sanna Hannonen: Investigation; Writing – review & editing.

Minna Rusanen: Validation; Writing – review & editing.

Juha-Matti Lehtola: Investigation; Writing – review & editing.

Toni Saari: Investigation; Validation; Writing – review & editing.

Hannu Uusitalo: Writing – review & editing.

Ville Leinonen: Funding acquisition; Resources; Writing – review & editing.

Bernd Thiede: Investigation; Methodology; Resources; Software; Writing – original draft; Writing – review & editing; proteomics study and analysis.

Kai Kaarniranta: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Writing – original draft; Writing – review &editing.

Anne Koivisto: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing – original draft; Writing – review & editing; PI of the study.

Tor Paaske Utheim: Investigation; Methodology; Resources; Software; Writing – original draft; Writing – review & editing; proteomic study and analysis.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

These data are not publicly available due to privacy or ethical restrictions.

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