Key Points
Question
What cortical proteins are associated with cognitive resilience among community-dwelling older persons?
Finding
This study leveraged data from 391 community-dwelling older persons to conduct a proteome-wide association analysis of the human dorsolateral prefrontal cortex. Eight cortical proteins were identified in association with cognitive resilience, of which a higher level of NRN1, ACTN4, EPHX4, RPH3A, SGTB, CPLX1, and SH3GL1 and a lower level of UBA1 were associated with greater resilience.
Meaning
Identifying these cortical proteins provides a complementary approach to developing novel therapeutics for the treatment and prevention of Alzheimer disease and related dementias.
Abstract
Importance
Identifying genes and proteins for cognitive resilience (ie, targets that may be associated with slowing or preventing cognitive decline regardless of the presence, number, or combination of common neuropathologic conditions) provides a complementary approach to developing novel therapeutics for the treatment and prevention of Alzheimer disease and related dementias.
Objective
To identify proteins associated with cognitive resilience via a proteome-wide association study of the human dorsolateral prefrontal cortex.
Design, Setting, and Participants
This study used data from 391 community-dwelling older persons who participated in the Religious Orders Study and the Rush Memory and Aging Project. The Religious Orders Study began enrollment January 1, 1994, and the Rush Memory and Aging Project began enrollment September 1, 1997, and data were collected and analyzed through October 23, 2019.
Exposures
Participants had undergone annual detailed clinical examinations, postmortem evaluations, and tandem mass tag proteomics analyses.
Main Outcomes and Measures
The outcome of cognitive resilience was defined as a longitudinal change in cognition over time after controlling for common age-related neuropathologic indices, including Alzheimer disease, Lewy bodies, transactive response DNA-binding protein 43, hippocampal sclerosis, infarcts, and vessel diseases. More than 8000 high abundance proteins were quantified from frozen dorsolateral prefrontal cortex tissue using tandem mass tag and liquid chromatography-mass spectrometry.
Results
There were 391 participants (273 women); their mean (SD) age was 79.7 (6.7) years at baseline and 89.2 (6.5) years at death. Eight cortical proteins were identified in association with cognitive resilience: a higher level of NRN1 (estimate, 0.140; SE, 0.024; P = 7.35 × 10−9), ACTN4 (estimate, 0.321; SE, 0.065; P = 9.94 × 10−7), EPHX4 (estimate, 0.198; SE, 0.042; P = 2.13 × 10−6), RPH3A (estimate, 0.148; SE, 0.031; P = 2.58 × 10−6), SGTB (estimate, 0.211; SE, 0.045; P = 3.28 × 10−6), CPLX1 (estimate, 0.136; SE, 0.029; P = 4.06 × 10−6), and SH3GL1 (estimate, 0.179; SE, 0.039; P = 4.21 × 10−6) and a lower level of UBA1 (estimate, −0.366; SE, 0.076; P = 1.43 × 10−6) were associated with greater resilience.
Conclusions and Relevance
These protein signals may represent novel targets for the maintenance of cognition in old age.
This cohort study uses data from participants in the Religious Orders Study and Rush Memory and Aging Project to identify proteins associated with cognitive resilience via a proteome-wide association study of the human dorsolateral prefrontal cortex.
Introduction
Alzheimer disease (AD), cerebrovascular diseases, Lewy body disease, and limbic-predominant age-associated transactive response DNA-binding protein 43 (TDP-43) encephalopathy are primary causes of dementia.1,2 However, these neuropathologic conditions are also commonly observed among elderly individuals who died without dementia or cognitive impairment.3 Data from clinical-pathologic studies show that only a portion of person-specific cognitive decline may be explained by common neuropathologic indices,4 suggesting that many other factors play a role. Although some factors, such as neurodegeneration, are downstream events of neuropathologic conditions, other factors are associated with cognitive decline without working through neuropathologic conditions. Of particular interest is that brains differ in their ability to tolerate various proteinopathies, vessel diseases, and vascular tissue injuries, and this ability is associated with cognitive resilience. Identifying genes and proteins for cognitive resilience (ie, targets that are associated with slowing or preventing cognitive decline regardless of the presence, number, or combination of common neuropathologic conditions) provides a complementary approach to developing new therapeutics for the treatment and prevention of AD and related dementias. Prior research has shown that neural reserve and synaptic proteins may act to maintain late-life cognition, above and beyond the association of neuropathologic conditions with cognition,5,6,7 while other genes may be associated with a reduction in the deleterious effect of AD pathologic conditions.8 Most of these studies target specific genes and proteins a priori, and it is possible that additional proteins associated with cognitive resilience await discovery.
In this study, we sought to identify novel human cortical proteins associated with cognitive resilience. We define cognitive resilience as change in cognition over time after controlling for common neuropathologic indices associated with AD and related dementias. In this context, the average person has average resilience, whereas some people can be more resilient and others less resilient. This approach has previously been used to identify genes and proteins associated with slower or faster rates of cognitive decline.9 We used tandem mass tag (TMT) and liquid chromatography-mass spectrometry to quantify more than 8000 high abundance proteins from frozen dorsolateral prefrontal cortex (DLPFC) tissue samples obtained from participants in 1 of 2 cohort studies with up to 25 annual waves of cognitive function data. We examined the association of individual proteins with cognitive resilience. We conducted additional analyses to extend the protein findings by examining the associations of cortical RNA gene expressions with cognitive resilience. Finally, we examined the association of proteins with individual neuropathologic indices.
Methods
Participants
Participants came from the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP). The ROS began enrollment January 1, 1994, and the MAP began enrollment September 1, 1997.10 The ROS and the MAP are conducted by the same team of investigators and share a large common core of measures, which allows for the efficient merging of the data for combined analyses. Participants enroll without a known diagnosis of dementia and agree to undergo annual detailed clinical and cognitive evaluations, as well as to donate their brains at death. Both studies were approved by the Rush University Medical Center institutional review board. At enrollment, each participant provided written informed consent and signed the Uniform Anatomical Gift Act.
Through October 23, 2019, a total of 1654 ROS and MAP participants had died and undergone brain autopsies. The present study focused on 400 persons who had TMT proteomics analyses performed using frozen tissue samples obtained from the DLPFC; statistical analyses were conducted using data from 391 persons who passed proteomic data quality control. Between the subset of individuals with TMT proteomic data and the overall ROS and MAP participants who underwent autopsy, the demographic characteristics are similar; in general, individuals with TMT data had fewer neuropathologic conditions (eTable 1 in the Supplement). Tandem mass tag data collection is ongoing.
Cognitive and Clinical Evaluations
Uniform structured cognitive and clinical evaluations are administered each year by examiners blinded to data from prior years (eAppendix 1 in the Supplement). An annual cognitive evaluation includes 19 cognitive performance tests that are in common between the 2 studies. The Mini-Mental State Examination is used for descriptive purposes, and the Complex Ideational Material from the Boston Diagnostic Aphasia Examination is used only in diagnostic classification. The remaining 17 tests are combined into a composite measure of global cognition. In brief, individual test scores were converted to z scores using the baseline mean (SD) value and then averaged to obtain the composite score.11
Postmortem Evaluations
Details of the postmortem evaluations have been previously reported.12,13 The neuropathologic evaluations systematically assessed common neurodegenerative and cerebrovascular conditions, including AD, Lewy bodies, TDP-43, hippocampal sclerosis, chronic macroscopic and microinfarcts, cerebral amyloid angiopathy, atherosclerosis, and arteriolosclerosis; the evaluations were conducted by examiners blinded to all clinical data (eAppendix 1 in the Supplement).
Mass Spectrometry–Based Proteomics Using Isobaric TMT
Tandem mass tag proteomics analysis was conducted on frozen tissue samples of the DLPFC (eAppendix 1 and eFigures 6 and 7 in the Supplement). Details on the mass spectrometry–based proteomics, database searches, and quality control have been previously described.14 In brief, the samples were homogenized, and the protein concentration was determined. After protein digestion, isobaric TMT peptide labeling and high pH fractionation were performed. Fractions were then analyzed by liquid chromatography-mass spectrometry. The resulting mass spectrometry spectra were searched against the UniProt human protein database, with individual protein abundance checked against the global internal standard. An additional data process included regressing out technical confounders. A total of 8356 proteins in 391 persons passed the final quality control.
Statistical Analysis
We first described the characteristics of the participants. Next, we performed a proteome-wide association analysis to examine the association of individual proteins with cognitive resilience by conducting 8356 parallel linear mixed-effects models (eAppendix 1 in the Supplement). To minimize false-positive results due to multiple testing, statistical significance was determined a priori at an α level of 5 × 10−6, corresponding to a Bonferroni correction for 10 000 tests. For proteins with significant associations, we extended the result by replacing protein abundance with the corresponding gene expression from RNA-Seq. Regression models examined the association of proteins with individual neuropathologic indices. For targeted analyses, statistical significance was determined at a nominal level of α = .05 unless otherwise specified.
The analyses were conducted using SAS/STAT software, version 9.4 (SAS Institute Inc). All the models were adjusted for age, sex, and educational level. The length of follow-ups, the time interval between last assessment and death, and the postmortem interval are not associated with cognitive resilience (all P > .10); therefore, we did not include these covariates for model parsimony.
Results
Characteristics of Study Individuals
Of the 391 older persons (273 women) included in this study, the mean (SD) baseline age was 79.7 (6.7) years, and the mean (SD) age at death was 89.2 (6.5) years (Table). During a mean (SD) of 8.7 (4.5) years of annual follow-up, there was an overall decline in cognition, and the median Mini-Mental State Examination score decreased from 29 (interquartile range, 28-30) at baseline to 26 (interquartile range, 20-28) proximate to death. Of the 391 participants, 122 (31.2%) received a diagnosis of Alzheimer dementia at death. At autopsy, 233 participants (59.6%) had a pathologic diagnosis of AD according to modified National Institute on Aging-Reagan criteria. Hippocampal sclerosis was present in 32 of the brains (8.2%), and neocortical Lewy bodies were present in 56 of the brains (14.3%). A total of 104 of the brains (26.6%) showed TDP-43 pathologic characteristics that extended beyond the amygdala. Cerebrovascular diseases were also common, including macroscopic infarcts (125 [32.0%]), microscopic infarcts (110 [28.1%]), moderate or severe amyloid angiopathy (104 [26.6%]), atherosclerosis (121 [30.9%]), and arteriolosclerosis (118 [30.2%]).
Table. Characteristics of 391 Study Participants.
| Characteristic | Statistics |
|---|---|
| Age baseline, mean (SD), y | 79.7 (6.7) |
| Age at death, mean (SD), y | 89.2 (6.5) |
| Male sex, No. (%) | 118 (30.2) |
| Educational level, mean (SD), y | 15.8 (3.6) |
| Length of follow-up, mean (SD), y | 8.7 (4.5) |
| MMSE score, median (IQR) | |
| At baseline | 29 (28-30) |
| At death | 26 (20-28) |
| Global cognition score, mean (SD) | |
| At baseline | 0.19 (0.38) |
| At death | –0.54 (1.03) |
| MCI, No. (%) | 101 (25.8) |
| Dementia, No. (%) | 130 (33.2) |
| NIA-Reagan AD, No. (%)a | 233 (59.6) |
| Global AD pathologic score, median (IQR) | 0.54 (0.14-1.00) |
| Neuritic plaques score, median (IQR) | 0.60 (0.02-1.15) |
| Diffuse plaques score, median (IQR) | 0.45 (0.05-1.05) |
| Neurofibrillary tangles score, median (IQR) | 0.29 (0.13-0.63) |
| Macroscopic infarcts, No. (%) | 125 (32.0) |
| Microinfarcts, No. (%) | 110 (28.1) |
| Neocortical Lewy bodies, No. (%) | 56 (14.3) |
| TDP-43, No. (%)b | 104 (26.6) |
| Hippocampal sclerosis, No. (%) | 32 (8.2) |
| Amyloid angiopathy, No. (%)c | 104 (26.6) |
| Atherosclerosis, No. (%)c | 121 (30.9) |
| Arteriolosclerosis, No. (%)c | 118 (30.2) |
Abbreviations: AD, Alzheimer disease; IQR, interquartile range; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; NIA, National Institute on Aging; TDP-43, transactive response DNA-binding protein 43.
Intermediate or high likelihood.
Inclusion beyond the amygdala.
Moderate or severe.
Cortical Proteins With Cognitive Resilience
We first examined the association of common neuropathologic conditions with cognitive decline. Multiple pathologic indices were independently associated with a faster rate of decline in cognition (eTable 2 in the Supplement). Age was not associated with cognitive decline with neuropathologic indices included in the model, consistent with a recent report that late-life cognitive loss reflects pathologic and mortality-related processes but not a normative age-related process.15 We next performed 8356 parallel analyses examining the association of individual cortical proteins with cognitive resilience (Figure 1). Neuritin (NRN1) was the lead protein associated with cognitive resilience after correction for multiple testing, and 7 other proteins, including alpha-actinin-4 (ACTN4), ubiquitin-like modifier-activating enzyme 1 (UBA1), epoxide hydrolase 4 (EPHX4), rabphilin-3A (RPH3A), small glutamine-rich tetratricopeptide repeat-containing protein beta (SGTB), complexin-1 (CPLX1), and endophilin-A2 (SH3GL1), were also associated with cognitive resilience (eTable 3 in the Supplement). Specifically, comparing older persons with the same age, sex, educational level, and neuropathologic burdens, those with higher levels of NRN1 (estimate, 0.140; SE, 0.024; P = 7.35 × 10−9), ACTN4 (estimate, 0.321; SE, 0.065; P = 9.94 × 10−7), EPHX4 (estimate, 0.198; SE, 0.042; P = 2.13 × 10−6), RPH3A (estimate, 0.148; SE, 0.031; P = 2.58 × 10−6), SGTB (estimate, 0.211; SE, 0.045; P = 3.28 × 10−6), CPLX1 (estimate, 0.136; SE, 0.029; P = 4.06 × 10−6), or SH3GL1 (estimate, 0.179; SE, 0.039; P = 4.21 × 10−6) had a slower rate of cognitive decline than older persons with lower levels of those proteins, and those with higher levels of UBA1 (estimate, −0.366; SE, 0.076; P = 1.43 × 10−6) had a faster rate of decline than those with lower UBA1 levels (Figure 2A-H).
Figure 1. Proteome-Wide Association of Cortical Proteins With Cognitive Resilience.
Each point on the plot represents the association of an individual protein with cognitive decline after controlling for age, sex, educational level, and 9 neuropathologic indices. The horizontal coordinate is the corresponding gene location within the chromosome (defined as the midpoint of the start and end position). The vertical coordinate is −log10 of the P value if the protein is associated with a slower rate of cognitive decline (ie, greater resilience) and log10 of the P value if it is associated with a faster rate of decline (ie, less resilience). The dashed lines correspond to the reference significance level representing α = 5 × 10−6. ACTN4 indicates alpha-actinin-4; CPLX1, complexin-1; EPHX4, epoxide hydrolase 4; NRN1, neuritin; RPH3A, rabphilin-3A; SGTB, small glutamine-rich tetratricopeptide repeat-containing protein beta; SH3GL1, endophilin-A2; and UBA1, ubiquitin-like modifier-activating enzyme 1.
Figure 2. Target Protein and RNA Expression Associations With Cognitive Resilience.
A-H, Associations of individual proteins with cognitive resilience. In each panel, the circles indicate cognitive resilience (ie, person-specific rates of decline estimated from linear mixed models adjusted for demographic characteristics and neuropathologic conditions) plotted against the protein level, with a corresponding regression line with 95% CI (shaded area). I, Associations of RNA expression of corresponding genes with cognitive resilience. The circles indicate the point estimates, the horizontal line segments indicate 95% CIs for gene expression associations with cognitive resilience, and the x-axis indicates the difference in resilience with every 1-unit increase in the gene expression level.
Cortical Gene Expressions With Cognitive Resilience
To further evaluate the cortical proteins associated with cognitive resilience, we examined the associations of the corresponding gene expressions with cognitive resilience. RNA-Seq data from the DLPFC were available from 1216 persons (eAppendix 1 in the Supplement). The correlations between the target proteins and corresponding gene expressions vary. Weak but significant correlations were observed for NRN1 (r = 0.23; P < .001), ACTN4 (r = 0.13; P = .01), RPH3A (r = 0.33; P < .001), SGTB (r = 0.34; P < .001), and CPLX1 (r = 0.31; P<.001). Consistent with the NRN1 protein result, we found a significant association of NRN1 (HGNC 17972) gene expression with cognitive resilience, such that after controlling for common neuropathologic conditions, a higher level of NRN1 gene expression was associated with a slower rate of cognitive decline (eTable 4 in the Supplement). Of the other 7 proteins associated with cognitive resilience, higher gene expressions of RPH3A (HGNC 17056), SGTB (HGNC 23567), and CPLX1 (HGNC 2309) were nominally associated with cognitive resilience (Figure 2I). Furthermore, the cis eQTL (expression quantitative trait loci) analyses identified multiple variants that were associated with CPLX1 and SGTB gene expressions (eAppendix 2 and eFigure 8 in the Supplement).
Cortical Proteins With Common Neuropathologic Conditions
We examined the associations of the target cortical proteins with common neuropathologic conditions in a series of regression analyses adjusted for demographic characteristics (Figure 3). We consider a cutoff of P < .005 to correct for testing multiple neuropathologic conditions. Six proteins (ie, NRN1, ACTN4, EPHX4, RPH3A, SGTB, and CPLX1) were associated with the global AD pathologic measure, such that older persons with a higher protein level had a lower burden of AD. In addition, UBA1 and SH3GL1 were also associated with neuritic plaques (eFigure 1 in the Supplement). The NRN1 and ACTN4 proteins were associated with lower odds of neocortical Lewy bodies. Separately, older persons with a higher RPH3A level had lower odds of having more severe atherosclerosis and arteriosclerosis. We did not observe other significant associations between proteins and pathologic conditions. We examined the strength of proteins’ association with cognitive decline before and after adjusting for 9 neuropathologic indices (eTable 5 in the Supplement). Between 63% and 80% of the overall protein association was above and beyond neuropathologic conditions, lending further support that these proteins are primarily involved in cognitive resilience.
Figure 3. Association of Target Proteins With Common Neuropathologic Indices.
Multivariable regression analysis was conducted with each of the neuropathologic indices as the outcome, adjusted for age, sex, and educational level. The y-axis shows the −log10 of the P values, and the height represents the significance of the P value. The higher the bar, the stronger the protein association with specific neuropathologic indices. The dashed horizontal line indicates P = .005. ACTN4 indicates alpha-actinin-4; AD, Alzheimer disease; CAA, cerebral amyloid angiopathy; CPLX1, complexin-1; EPHX4, epoxide hydrolase 4; NRN1, neuritin; RPH3A, rabphilin-3A; SGTB, small glutamine-rich tetratricopeptide repeat-containing protein beta; SH3GL1, endophilin-A2; TDP-43, transactive response DNA-binding protein 43; and UBA1, ubiquitin-like modifier-activating enzyme 1.
NRN1 and BDNF Gene Expression With Cognitive Resilience
The current result on NRN1 protein complements a prior finding that cortical gene expression of BDNF (HGNC 1033), another neurotrophic factor, was also associated with cognitive resilience.8 It has been reported that NRN1 expression is induced by BDNF.16 Our gene expression data show a positive correlation between cortical NRN1 and BDNF (Pearson r = 0.64; P < .001). BDNF was associated with cognitive resilience such that, after adjustment for the neuropathologic indices, a higher level of BDNF expression was associated with a slower rate of cognitive decline (estimate, 0.008; SE, 0.003; P = .005). With NRN1 expression added to the model, the BDNF association was attenuated and no longer significant (estimate, −0.0001; SE, 0.004; P = .97). On the other hand, NRN1 expression remained associated with a slower rate of cognitive decline (estimate, 0.026; SE, 0.007; P < .001). This result supports NRN1 serving as a mediator for BDNF in association with cognitive resilience (eFigure 2 in the Supplement).
Discussion
We conducted a proteome-wide association study to identify human cortical proteins associated with cognitive resilience. We identified 8 proteins, many of which are implicated in aging and neurodegenerative conditions. Despite the fact that the associations of these proteins with cognitive decline were identified after controlling for common neuropathologic conditions, they were also associated with AD pathologic indices. In particular, the association of a global AD pathologic measure with cognitive decline was weaker in brains with higher SGTB levels (eAppendix 2 and eFigure 3 in the Supplement). Together, our results support that these cortical proteins represent novel therapeutic targets for the treatment and prevention of AD and related dementias.
Developing robust strategies to combat AD and related dementias requires a thorough understanding of the pathologic basis underlying late-life cognitive decline. The field has increasingly recognized the neuropathologic heterogeneity of cognitive decline and Alzheimer dementia.13,17 Such heterogeneity makes it impractical and likely cost prohibitive to identify biomarkers for individual pathologic conditions and then develop therapeutic agents to target all pathologic conditions.18 By contrast, identifying gene and protein targets that promote resilience provides a complementary approach to protect cognitive health in old age. These targets have the potential to offset the detrimental effects of any number and/or combination of mixed pathologic conditions without relying on expensive and hard-to-get biomarkers for individual pathologic conditions or combined therapeutics.
Of the 8 cortical proteins associated with cognitive resilience, NRN1 was the strongest signal. It is known that the neurotrophin family plays an important role in synaptic function and plasticity, as well as regulating neural function.19 Neuritin is a neurotrophic factor predominantly expressed in the brain and in the hippocampus in particular.16 Neuritin promotes axon regeneration through neuritogenesis, neurite arborization, and extension.20 A previous study examined the proteomic signature of cognitive resilience in 2 smaller but independent cohorts of aging participants and found that a higher NRN1 level was associated with a slower rate of cognitive decline after adjusting for AD pathologic conditions.21 Our present study extends these findings by showing that cortical neuritin was associated with a slower rate of late-life cognitive decline with accumulating AD as well as other non-AD neuropathologic conditions. Many neurotrophin-related genes, including NRN1, could be induced or regulated by external stimulation including exercise, light, and sensory experience.22,23,24
Neuritin is implicated in neuropsychiatric disorders, and a variant in NRN1 is associated with depressive symptoms.25 Given a prior report26 that the association of late-life depression with cognitive decline is not associated with dementia-related neuropathologic conditions, we examined whether the association of depressive symptoms with cognitive decline works through neuritin. We found that only a small proportion of this association may be attributable to the NRN1 protein (eAppendix 2 and eTable 6 in the Supplement).
Data from cell cultures demonstrate that neuritin can be induced by exogenous BDNF, suggesting that neuritin is downstream of BDNF and acts to mediate the effects of BDNF.16 Our results for NRN1 and BDNF gene expression in the human cortex extend the previous finding. The expression of the 2 genes is positively correlated, and the association of BDNF expression with cognitive resilience is mediated by NRN1. The result provides new supporting evidence that BDNF is associated with cognitive resilience through NRN1, which has potential translational implications in target selection.
Our analysis has identified 7 other proteins associated with cognitive resilience, not all of which promote neurogenesis. ACTN4 is among the mesenchymal stem cell proteins reportedly regulated during Schwann cell transdifferentiation.27 Transdifferentiated mesenchymal stem cells secrete significant amounts of BDNF and nerve growth factor (NGF) in cell-conditioned media that facilitate neurite outgrowth. UBA1 has been associated with neurodegenerative diseases.28,29 In cultured hippocampal neurons, inhibition of UBA1 led to increased miniature and spontaneous synaptic currents at both excitatory and inhibitory synapses.30 RPH3A plays an important role in neurotransmitter release and synaptic vesicle trafficking, and cortical RPH3A deficits are implicated in various neurodegenerative conditions, including AD.31,32,33 EPHX4 belongs to the epoxide hydrolase family and acts as a high-activity epoxide hydrolase for fatty acids.34 The protein is preferentially expressed in the brain, yet its role in neurodegeneration is unclear. SGTB is associated with cellular functions by binding to a variety of molecules.35 A recent study reported that SGTB regulates a guidance receptor BOC (brother of CDO [cell adhesion molecule–related/down regulated by oncogenesis]) to promote neurite outgrowth.36 Our data show that the association of AD with late-life cognitive decline is weaker in brains with higher SGTB levels. CPLX1 is a presynaptic protein that binds to the SNAP (soluble N-ethylmaleimide-sensitive fusion protein attachment protein) receptor and modulates neurotransmitter release. A previous study found that a higher cortical CPLX1 level was associated with better cognition and lower odds of dementia, above and beyond the effects of AD and cerebral infarcts.5 A higher CPLX1 to CPLX2 ratio was associated with better cognition and less decline over time.37
Postmortem magnetic resonance imaging indices of brain tissue integrity complement traditional histopathologic measures and serve as a powerful tool for elucidating the neurobiology underlying cognitive impairment. The transverse relaxation rate (R2), for instance, measures neural tissue integrity by assessing the tissue’s unbound water content and the presence of paramagnetic materials such as iron. Higher R2 is associated with a slower rate of cognitive decline not accounted for by common neuropathologic conditions.38 Prior evidence further suggests that R2 mediates the associations of select cortical proteins with cognitive decline.39 We examined the extent to which R2 is implicated in the target protein associations with cognitive resilience. We found that both proteins and R2 are associated with cognitive resilience when included in the same model, and there is no apparent attenuation of the protein association (eAppendix 2, eTable 8, and eFigure 9 in the Supplement). These results suggest that the protein signals observed in the present study are relatively independent of the R2 measure.
Strengths and Limitations
Our study has several strengths. The data come from 2 prospective cohort studies with high follow-up and autopsy rates. Proteome-wide association analysis links thousands of high abundance cortical proteins to change in cognition over many years prior to death while controlling for common causes of dementia. The approach provides a comprehensive analysis of protein signals that are associated with cognitive resilience. Stringent Bonferroni correction for multiple testing reduces the chance of spurious findings. The protein signals are robust against potential confounders such as medications (eAppendix 2 and eTable 7 in the Supplement). Confidence in the veracity of the protein results is further increased by replicating the association using gene expression data from the same DLPFC.
This study also has some limitations, including that ROS and MAP are voluntary cohorts and enrollment requires consent for organ donation. Participants are older and have a higher educational level, and a majority are non-Latino white individuals. Findings in this study will need to be replicated in other cohorts as well as other races/ethnicities. Although the linear trajectory fit the data reasonably well (eFigures 4 and 5 in the Supplement), the approach does not address the nonlinear cognitive trajectories common in late life. Neuropathologic conditions are assessed in multiple brain regions, while the tissue collection site for TMT proteomics is restricted to the DLPFC. This approach lacks an intuitive appeal of matching the 2 spatially.
Conclusions
Identifying genes and proteins that promote cognitive resilience opens up new avenues to protect cognitive health in old age. In the present study, we identified 8 protein signals in the human cortex that are associated with late life cognitive decline above and beyond the effect of common neuropathologic conditions. These protein signals may represent novel therapeutic targets for the treatment and prevention of AD and related dementias.
eAppendix 1. Methods
eAppendix 2. Results
eReferences
eTable 1. Characteristics of ROS and MAP Autopsied Participants
eTable 2. Associations of Demographics and Common Neuropathologies With Cognitive Decline
eTable 3. Associations of Cortical Proteins With Cognitive Resilience (Top 100)
eTable 4. Associations of Target Gene Expression With Cognitive Resilience
eTable 5. Associations of Target Proteins With Cognitive Decline With and Without Adjustment for Neuropathologies
eTable 6. Associations of Depressive Symptoms With Cognitive Resilience With and Without Adjustment for Target Proteins
eTable 7. Associations of Target Proteins With Cognitive Resilience With and Without Adjustment for Medications
eTable 8. Associations of Target Proteins With Cognitive Resilience With and Without Adjustment for Postmortem Transverse Relaxation Rate
eFigure 1. Association of Target Proteins With Common Neuropathologies
eFigure 2. Association Between BDNF, NRN1 Gene Expressions, and Cognitive Resilience
eFigure 3. Association of AD Pathology With Cognitive Decline by SGTB Protein
eFigure 4. Linear Model Fit for Cognitive Decline (a Random Sample)
eFigure 5. Person-Specific Model Fit for Cognitive Decline (a Random Sample)
eFigure 6. MS3 vs MS2 Based TMT Reporter Tag Quantification
eFigure 7. PCA Plots Before and After Adjustment of Batch and MS Mode
eFigure 8. Cis eQTL of SGTB, CPLX1, NRN1 and RPH3A
eFigure 9. Correlations of Target Cortical Proteins and Postmortem Transverse Relaxation Rate
References
- 1.Gearing M, Mirra SS, Hedreen JC, Sumi SM, Hansen LA, Heyman A. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), part X: neuropathology confirmation of the clinical diagnosis of Alzheimer’s disease. Neurology. 1995;45(3, pt 1):461-466. doi: 10.1212/WNL.45.3.461 [DOI] [PubMed] [Google Scholar]
- 2.Dolan D, Troncoso J, Resnick SM, Crain BJ, Zonderman AB, O’Brien RJ. Age, Alzheimer’s disease and dementia in the Baltimore Longitudinal Study of Ageing. Brain. 2010;133(pt 8):2225-2231. doi: 10.1093/brain/awq141 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bennett DA, Wilson RS, Boyle PA, Buchman AS, Schneider JA. Relation of neuropathology to cognition in persons without cognitive impairment. Ann Neurol. 2012;72(4):599-609. doi: 10.1002/ana.23654 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Boyle PA, Wilson RS, Yu L, et al. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann Neurol. 2013;74(3):478-489. doi: 10.1002/ana.23964 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Honer WG, Barr AM, Sawada K, et al. Cognitive reserve, presynaptic proteins and dementia in the elderly. Transl Psychiatry. 2012;2:e114. doi: 10.1038/tp.2012.38 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Head E, Corrada MM, Kahle-Wrobleski K, et al. Synaptic proteins, neuropathology and cognitive status in the oldest-old. Neurobiol Aging. 2009;30(7):1125-1134. doi: 10.1016/j.neurobiolaging.2007.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Wilson RS, Nag S, Boyle PA, et al. Neural reserve, neuronal density in the locus ceruleus, and cognitive decline. Neurology. 2013;80(13):1202-1208. doi: 10.1212/WNL.0b013e3182897103 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Buchman AS, Yu L, Boyle PA, Schneider JA, De Jager PL, Bennett DA. Higher brain BDNF gene expression is associated with slower cognitive decline in older adults. Neurology. 2016;86(8):735-741. doi: 10.1212/WNL.0000000000002387 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Yu L, Petyuk VA, Gaiteri C, et al. Targeted brain proteomics uncover multiple pathways to Alzheimer’s dementia. Ann Neurol. 2018;84(1):78-88. doi: 10.1002/ana.25266 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. J Alzheimers Dis. 2018;64(s1):S161-S189. doi: 10.3233/JAD-179939 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wilson RS, Boyle PA, Yang J, James BD, Bennett DA. Early life instruction in foreign language and music and incidence of mild cognitive impairment. Neuropsychology. 2015;29(2):292-302. doi: 10.1037/neu0000129 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Schneider JA, Arvanitakis Z, Leurgans SE, Bennett DA. The neuropathology of probable Alzheimer disease and mild cognitive impairment. Ann Neurol. 2009;66(2):200-208. doi: 10.1002/ana.21706 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Boyle PA, Yu L, Wilson RS, Leurgans SE, Schneider JA, Bennett DA. Person-specific contribution of neuropathologies to cognitive loss in old age. Ann Neurol. 2018;83(1):74-83. doi: 10.1002/ana.25123 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Wingo AP, Fan W, Duong DM, et al. Cerebral atherosclerosis contributes to Alzheimer’s dementia independently of its hallmark amyloid and tau pathologies. Posted October 13, 2019. bioRxiv 793349. doi: 10.1101/793349 [DOI] [Google Scholar]
- 15.Wilson RS, Wang T, Yu L, Bennett DA, Boyle PA. Normative cognitive decline in old age. Ann Neurol. Published online March 6, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Naeve GS, Ramakrishnan M, Kramer R, Hevroni D, Citri Y, Theill LE. Neuritin: a gene induced by neural activity and neurotrophins that promotes neuritogenesis. Proc Natl Acad Sci U S A. 1997;94(6):2648-2653. doi: 10.1073/pnas.94.6.2648 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.White L, Small BJ, Petrovitch H, et al. Recent clinical-pathologic research on the causes of dementia in late life: update from the Honolulu-Asia Aging Study. J Geriatr Psychiatry Neurol. 2005;18(4):224-227. doi: 10.1177/0891988705281872 [DOI] [PubMed] [Google Scholar]
- 18.Bennett DA. Mixed pathologies and neural reserve: implications of complexity for Alzheimer disease drug discovery. PLoS Med. 2017;14(3):e1002256. doi: 10.1371/journal.pmed.1002256 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Huang EJ, Reichardt LF. Neurotrophins: roles in neuronal development and function. Annu Rev Neurosci. 2001;24:677-736. doi: 10.1146/annurev.neuro.24.1.677 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Shimada T, Sugiura H, Yamagata K. Neuritin: a therapeutic candidate for promoting axonal regeneration. World J Neurol. 2013;3(4):138-143. doi: 10.5316/wjn.v3.i4.138 [DOI] [Google Scholar]
- 21.Wingo AP, Dammer EB, Breen MS, et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat Commun. 2019;10(1):1619. doi: 10.1038/s41467-019-09613-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hunsberger JG, Newton SS, Bennett AH, et al. Antidepressant actions of the exercise-regulated gene VGF. Nat Med. 2007;13(12):1476-1482. doi: 10.1038/nm1669 [DOI] [PubMed] [Google Scholar]
- 23.Nedivi E, Fieldust S, Theill LE, Hevron D. A set of genes expressed in response to light in the adult cerebral cortex and regulated during development. Proc Natl Acad Sci U S A. 1996;93(5):2048-2053. doi: 10.1073/pnas.93.5.2048 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Harwell C, Burbach B, Svoboda K, Nedivi E. Regulation of cpg15 expression during single whisker experience in the barrel cortex of adult mice. J Neurobiol. 2005;65(1):85-96. doi: 10.1002/neu.20176 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Prats C, Arias B, Ortet G, et al. Neurotrophins role in depressive symptoms and executive function performance: association analysis of NRN1 gene and its interaction with BDNF gene in a non-clinical sample. J Affect Disord. 2017;211:92-98. doi: 10.1016/j.jad.2016.11.017 [DOI] [PubMed] [Google Scholar]
- 26.Wilson RS, Capuano AW, Boyle PA, et al. Clinical-pathologic study of depressive symptoms and cognitive decline in old age. Neurology. 2014;83(8):702-709. doi: 10.1212/WNL.0000000000000715 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Sharma AD, Wiederin J, Uz M, et al. Proteomic analysis of mesenchymal to Schwann cell transdifferentiation. J Proteomics. 2017;165:93-101. doi: 10.1016/j.jprot.2017.06.011 [DOI] [PubMed] [Google Scholar]
- 28.Groen EJN, Gillingwater TH. UBA1: at the crossroads of ubiquitin homeostasis and neurodegeneration. Trends Mol Med. 2015;21(10):622-632. doi: 10.1016/j.molmed.2015.08.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Teuling E, Bourgonje A, Veenje S, et al. Modifiers of mutant huntingtin aggregation: functional conservation of C. elegans-modifiers of polyglutamine aggregation. PLoS Curr. 2011;3:RRN1255. doi: 10.1371/currents.RRN1255 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rinetti GV, Schweizer FE. Ubiquitination acutely regulates presynaptic neurotransmitter release in mammalian neurons. J Neurosci. 2010;30(9):3157-3166. doi: 10.1523/JNEUROSCI.3712-09.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Smith R, Klein P, Koc-Schmitz Y, et al. Loss of SNAP-25 and rabphilin 3a in sensory-motor cortex in Huntington’s disease. J Neurochem. 2007;103(1):115-123. [DOI] [PubMed] [Google Scholar]
- 32.Tan MG, Lee C, Lee JH, et al. Decreased rabphilin 3A immunoreactivity in Alzheimer’s disease is associated with Aβ burden. Neurochem Int. 2014;64:29-36. doi: 10.1016/j.neuint.2013.10.013 [DOI] [PubMed] [Google Scholar]
- 33.Dalfó E, Barrachina M, Rosa JL, Ambrosio S, Ferrer I. Abnormal α-synuclein interactions with rab3a and rabphilin in diffuse Lewy body disease. Neurobiol Dis. 2004;16(1):92-97. doi: 10.1016/j.nbd.2004.01.001 [DOI] [PubMed] [Google Scholar]
- 34.Decker M, Adamska M, Cronin A, et al. EH3 (ABHD9): the first member of a new epoxide hydrolase family with high activity for fatty acid epoxides. J Lipid Res. 2012;53(10):2038-2045. doi: 10.1194/jlr.M024448 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Cao M, Xu W, Yu J, et al. Up-regulation of SGTB is associated with neuronal apoptosis after neuroinflammation induced by lipopolysaccharide. J Mol Histol. 2013;44(5):507-518. doi: 10.1007/s10735-013-9517-4 [DOI] [PubMed] [Google Scholar]
- 36.Vuong TA, Lee SJ, Leem YE, Lee JR, Bae GU, Kang JS. SGTb regulates a surface localization of a guidance receptor BOC to promote neurite outgrowth. Cell Signal. 2019;55:100-108. doi: 10.1016/j.cellsig.2019.01.003 [DOI] [PubMed] [Google Scholar]
- 37.Ramos-Miguel A, Jones AA, Sawada K, et al. Frontotemporal dysregulation of the SNARE protein interactome is associated with faster cognitive decline in old age. Neurobiol Dis. 2018;114:31-44. doi: 10.1016/j.nbd.2018.02.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Dawe RJ, Yu L, Leurgans SE, et al. Postmortem MRI: a novel window into the neurobiology of late life cognitive decline. Neurobiol Aging. 2016;45:169-177. doi: 10.1016/j.neurobiolaging.2016.05.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Kim N, Yu L, Dawe R, et al. Microstructural changes in the brain mediate the association of AK4, IGFBP5, HSPB2, and ITPK1 with cognitive decline. Neurobiol Aging. 2019;84:17-25. doi: 10.1016/j.neurobiolaging.2019.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eAppendix 1. Methods
eAppendix 2. Results
eReferences
eTable 1. Characteristics of ROS and MAP Autopsied Participants
eTable 2. Associations of Demographics and Common Neuropathologies With Cognitive Decline
eTable 3. Associations of Cortical Proteins With Cognitive Resilience (Top 100)
eTable 4. Associations of Target Gene Expression With Cognitive Resilience
eTable 5. Associations of Target Proteins With Cognitive Decline With and Without Adjustment for Neuropathologies
eTable 6. Associations of Depressive Symptoms With Cognitive Resilience With and Without Adjustment for Target Proteins
eTable 7. Associations of Target Proteins With Cognitive Resilience With and Without Adjustment for Medications
eTable 8. Associations of Target Proteins With Cognitive Resilience With and Without Adjustment for Postmortem Transverse Relaxation Rate
eFigure 1. Association of Target Proteins With Common Neuropathologies
eFigure 2. Association Between BDNF, NRN1 Gene Expressions, and Cognitive Resilience
eFigure 3. Association of AD Pathology With Cognitive Decline by SGTB Protein
eFigure 4. Linear Model Fit for Cognitive Decline (a Random Sample)
eFigure 5. Person-Specific Model Fit for Cognitive Decline (a Random Sample)
eFigure 6. MS3 vs MS2 Based TMT Reporter Tag Quantification
eFigure 7. PCA Plots Before and After Adjustment of Batch and MS Mode
eFigure 8. Cis eQTL of SGTB, CPLX1, NRN1 and RPH3A
eFigure 9. Correlations of Target Cortical Proteins and Postmortem Transverse Relaxation Rate