Proteome-wide Association Study Research Articles

Integrative Analysis of Proteome-wide Association Studies and Functional Enrichment Analysis to Identify Genes and Chemicals Associated with Alcohol Dependence.

Alcohol dependence accounts for a large proportion of the global burden of disease and disability. This study aims to investigate the candidate genes and chemicals associated with alcohol dependence. Using data from published alcohol dependence genome-wide association studies, we first conducted a proteome-wide association study of alcohol dependence by integrating alcohol dependence genome-wide association studies with 2 human brain reference proteomes of dorsolateral prefrontal cortex from the Religious Order Study and Rush Memory and Aging Project and the Banner Sun Health Research Institute. Then, based on the identified genes in proteome-wide association study, we conducted functional enrichment analysis and chemical-related functional enrichment analysis to detect the related Gene Ontology terms and chemicals. Proteome-wide association study identified several potential candidate genes for alcohol dependence, such as GOT2 ( P = 7.59 × 10 -6 ) and C3orf33 ( P = 5.00 × 10 -3 ). Furthermore, functional enrichment analysis identified multiple candidate Gene Ontology terms associated with alcohol dependence, such as glyoxylate metabolic process (adjusted P = 2.99 × 10 -6 ) and oxoglutarate metabolic process (adjusted P = 9.95 × 10 -6 ). Chemical-related functional enrichment analysis detected several alcohol dependence-related candidate chemicals, such as pitavastatin ( P = 2.00 × 10 -4 ), cannabinoids ( P = 4.00 × 10 -4 ), 11-nor-Δ(9)-tetrahydrocannabinol-9-carboxylic acid ( P = 4.00 × 10 -4 ), and gabapentin ( P = 2.00 × 10 -3 ). Our study reports multiple candidate genes and chemicals associated with alcohol dependence, providing novel clues for understanding the biological mechanism of alcohol dependence.

The genetic structure of pain in depression patients: A genome-wide association study and proteome-wide association study

BackgroundComparing with the general population, the pain in depression patients has more complex biological mechanism. We aim to explore the etiological mechanism of pain in depression patients from the perspective of genetics. MethodsUtilizing the UK Biobank samples with self-reported depression status or PHQ score ≥10, we conducted genome-wide association studies (GWAS) of seven pain traits (N = 1,133–58,349). Additionally, we used FUSION pipeline to perform proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) by integrating GWAS summary data with two different proteome reference weights (ROS/MAP and Banner) and Rnaseq gene expression reference weights, respectively. ResultsGWAS identified 3 significant genes associated with different pain traits in depression patients, including TRIOBP (PGWAS = 4.48 × 10−8) for stomach or abdominal pain, SLC9A9(PGWAS = 2.77 × 10−8) for multisite chronic pain (MCP) and ADGRF1 (PGWAS = 1.51 × 10−8) for neck or shoulder pain. In addition, PWAS and TWAS analysis also identified multiple candidate genes associated with different pain traits in depression patients, such as TPRG1L (PPWAS-Banner = 3.38 × 10−2) and SIRPA (PPWAS-Banner = 3.65 × 10−2) for MCP, etc. Notably, when comparing the results of PWAS and TWAS analysis, we found overlapping candidate genes in these pain traits, such as GSTM3 (PPWAS-Banner = 3.38 × 10−2, PTWAS = 6.92 × 10−3) in the stomach or abdominal pain phenotype, ATG7 (PPWAS-Rosmap = 3.15 × 10−2, PTWAS = 2.98 × 10−2) in the MCP, etc. ConclusionsWe identified multiple novel candidate genes for pain traits in depression patients from different perspectives of genetics, which provided novel clues for understanding the genetic mechanisms underlying the pain in depression patients.

Integrated analysis of proteome-wide and transcriptome-wide association studies identified novel genes and chemicals for vertigo.

Vertigo is a leading symptom of various peripheral and central vestibular disorders. Although genome-wide association studies (GWASs) have identified multiple risk variants for vertigo, how these risk variants contribute to the risk of vertigo remains unknown. Discovery proteome-wide association study (PWAS) was first performed by integrating the protein quantitative trait loci from the dorsolateral prefrontal cortex (DLPFC) in the Banner Sun Health Research Institute dataset (n = 152) and GWAS summary of vertigo (n = 942 613), followed by replication PWAS using the protein quantitative trait loci from the DLPFC in Religious Orders Study or the Rush Memory and Aging Project dataset (n = 376). Transcriptome-wide association studies (TWASs) were then performed by integrating the same GWAS datasets of vertigo (n = 942 613) with mRNA expression reference from human fetal brain, and DLPFC. Chemical-related gene set enrichment analysis (GSEA) and Gene ontology/Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were finally conducted to further reveal the pathogenesis of vertigo. Permutation-based empirical P values were calculated in PWAS, TWAS, and GSEA. By integrating the GWAS of vertigo and two independent brain proteomes from human DLPFC, three genes were identified to genetically regulate protein abundance levels in vertigo, and were not previously implicated by GWAS, including MTERFD2 (P Banner = 0.045, P ROSMAP = 0.031), MGST1 (P Banner = 0.014, P ROSMAP = 0.018), and RAB3B (P Banner = 0.045, P ROSMAP = 0.035). Compared with TWAS results, we identified overlapping genes RAB3B (P TWAS = 0.017) and MTERFD2 (P TWAS = 0.003) that showed significant associations with vertigo at both proteome-wide and transcriptome-wide levels. Chemical-related GSEA identified multiple chemicals that might be associated with vertigo, such as nickel (P = 0.007), glycidamide (P = 0.005), and proanthocyanidins (P = 0.015). Our study provides novel clues for understanding the biological mechanism of vertigo, and highlights several possible risks and therapeutic chemicals for vertigo.

Integrating human brain proteomic data with genome-wide association study findings identifies novel brain proteins in substance use traits.

Despite the identification of a growing number of genetic risk loci for substance use traits (SUTs), the impact of these loci on protein abundance and the potential utility of relevant proteins as therapeutic targets are unknown. We conducted a proteome-wide association study (PWAS) in which we integrated human brain proteomes from discovery (Banner; N = 152) and validation (ROSMAP; N = 376) datasets with genome-wide association study (GWAS) summary statistics for 4 SUTs. The 4 samples comprised GWAS of European-ancestry individuals for smoking initiation [Smk] (N = 1,232,091), alcohol use disorder [AUD] (N = 313,959), cannabis use disorder [CUD] (N = 384,032), and opioid use disorder [OUD] (N = 302,585). We conducted transcriptome-wide association studies (TWAS) with human brain transcriptomic data to examine the overlap of genetic effects at the proteomic and transcriptomic levels and characterize significant genes through conditional, colocalization, and fine-mapping analyses. We identified 27 genes (Smk = 21, AUD = 3, CUD = 2, OUD = 1) that were significantly associated with cis-regulated brain protein abundance. Of these, 7 showed evidence for causality (Smk: NT5C2, GMPPB, NQO1, RHOT2, SRR and ACTR1B; and AUD: CTNND1). Cis-regulated transcript levels for 8 genes (Smk = 6, CUD = 1, OUD = 1) were associated with SUTs, indicating that genetic loci could confer risk for these SUTs by modulating both gene expression and proteomic abundance. Functional studies of the high-confidence risk proteins identified here are needed to determine whether they are modifiable targets and useful in developing medications and biomarkers for these SUTs.

Brain Proteome-Wide Association Study Identifies Candidate Genes that Regulate Protein Abundance Associated with Post-Traumatic Stress Disorder.

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (pPWAS-ROS/MAP = 3.00 × 10−5) and C3orf18 (pPWAS-Banner = 7.07 × 10−31). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (pTWAS-Splicing = 3.84 × 10−2), CHMP1A (pTWAS-Rnaseq = 5.09 × 10−4), and SIRT5 (pTWAS-Splicing = 4.81 × 10−3). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (pPWAS-Banner = 4.90 × 10−2, pTWAS-Splicing = 1.23 × 10−2) in the total population and GLO1(pPWAS-Banner = 4.89 × 10−3, pTWAS-Rnaseq = 1.41 × 10−3) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.

Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder.

Genome-wide association studies (GWAS) have identified several risk loci for post-traumatic stress disorder (PTSD); however, how they confer PTSD risk remains unclear. We aimed to identify genes that confer PTSD risk through their effects on brain protein abundance to provide new insights into PTSD pathogenesis. To that end, we integrated human brain proteomes with PTSD GWAS results to perform a proteome-wide association study (PWAS) of PTSD, followed by Mendelian randomization, using a discovery and confirmatory study design. Brain proteomes (N=525) were profiled from the dorsolateral prefrontal cortex using mass spectrometry. The Million Veteran Program (MVP) PTSD GWAS (n=186,689) was used for the discovery PWAS, and the Psychiatric Genomics Consortium PTSD GWAS (n=174,659) was used for the confirmatory PWAS. To understand whether genes identified at the protein-level were also evident at the transcript-level, we performed a transcriptome-wide association study (TWAS) using human brain transcriptomes (N=888) and the MVP PTSD GWAS results. We identified 11 genes that contribute to PTSD pathogenesis via their respective cis-regulated brain protein abundance. Seven of 11 genes (64%) replicated in the confirmatory PWAS and 4 of 11 also had their cis-regulated brain mRNA levels associated with PTSD. High confidence level was assigned to 9 of 11 genes after considering evidence from the confirmatory PWAS and TWAS. Most of the identified genes are expressed in other PTSD-relevant brain regions and several are preferentially expressed in excitatory neurons, astrocytes, and oligodendrocyte precursor cells. These genes are novel, promising targets for mechanistic and therapeutic studies to find new treatments for PTSD.

Identifying causal genes for stroke via integrating the proteome and transcriptome from brain and blood

BackgroundGenome-wide association studies (GWAS) have revealed numerous loci associated with stroke. However, the underlying mechanisms at these loci in the pathogenesis of stroke and effective stroke drug targets are elusive. Therefore, we aimed to identify causal genes in the pathogenesis of stroke and its subtypes.MethodsUtilizing multidimensional high-throughput data generated, we integrated proteome-wide association study (PWAS), transcriptome-wide association study (TWAS), Mendelian randomization (MR), and Bayesian colocalization analysis to prioritize genes that contribute to stroke and its subtypes risk via affecting their expression and protein abundance in brain and blood.ResultsOur integrative analysis revealed that ICA1L was associated with small-vessel stroke (SVS), according to robust evidence at both protein and transcriptional levels based on brain-derived data. We also identified NBEAL1 that was causally related to SVS via its cis-regulated brain expression level. In blood, we identified 5 genes (MMP12, SCARF1, ABO, F11, and CKAP2) that had causal relationships with stroke and stroke subtypes.ConclusionsTogether, via using an integrative analysis to deal with multidimensional data, we prioritized causal genes in the pathogenesis of SVS, which offered hints for future biological and therapeutic studies.

ICA1L Is Associated with Small Vessel Disease: A Proteome-Wide Association Study in Small Vessel Stroke and Intracerebral Haemorrhage.

Small vessel strokes (SVS) and intracerebral haemorrhages (ICH) are acute outcomes of cerebral small vessel disease (SVD). Genetic studies combining both phenotypes have identified three loci associated with both traits. However, the genetic cis-regulation at the protein level associated with SVD has not been studied before. We performed a proteome-wide association study (PWAS) using FUSION to integrate a genome-wide association study (GWAS) and brain proteomic data to discover the common mechanisms regulating both SVS and ICH. Dorsolateral prefrontal cortex (dPFC) brain proteomes from the ROS/MAP study (N = 376 subjects and 1443 proteins) and the summary statistics for the SVS GWAS from the MEGASTROKE study (N = 237,511) and multi-trait analysis of GWAS (MTAG)-ICH–SVS from Chung et al. (N = 240,269) were selected. We performed PWAS and then a co-localization analysis with COLOC. The significant and nominal results were validated using a replication dPFC proteome (N = 152). The replicated results (q-value < 0.05) were further investigated for the causality relationship using summary data-based Mendelian randomization (SMR). One protein (ICA1L) was significantly associated with SVS (z-score = −4.42 and p-value = 9.6 × 10−6) and non-lobar ICH (z-score = −4.8 and p-value = 1.58 × 10−6) in the discovery PWAS, with a high co-localization posterior probability of 4. In the validation PWAS, ICA1L remained significantly associated with both traits. The SMR results for ICA1L indicated a causal association of protein expression levels in the brain with SVS (p-value = 3.66 × 10−5) and non-lobar ICH (p-value = 1.81 × 10−5). Our results show that the association of ICA1L with SVS and non-lobar ICH is conditioned by the cis-regulation of its protein levels in the brain.

Genome-Wide Association Study and Genetic Correlation Scan Provide Insights into Its Genetic Architecture of Sleep Health Score in the UK Biobank Cohort

PurposeMost previous genetic studies of sleep behaviors were conducted individually, without comprehensive consideration of the complexity of various sleep behaviors. Our aim is to identify the genetic architecture and potential biomarker of the sleep health score, which more powerfully represents overall sleep traits.Patients and MethodsWe conducted a genome-wide association study (GWAS) of sleep health score (overall assessment of sleep duration, snoring, insomnia, chronotype, and daytime dozing) using 336,463 participants from the UK Biobank. Proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) were then performed to identify candidate genes at the protein and mRNA level, respectively. We finally used linkage disequilibrium score regression (LDSC) to estimate the genetic correlations between sleep health score and other functionally relevance traits.ResultsGWAS identified multiple variants near known candidate genes associated with sleep health score, such as MEIS1, FBXL13, MED20 and SMAD5. HDHD2 (PPWAS = 0.0146) and GFAP (PPWAS = 0.0236) were identified associated with sleep health score by PWAS. TWAS identified ORC4 (PTWAS = 0.0212) and ZNF732 (PTWAS = 0.0349) considering mRNA expression level. LDSC found significant genetic correlations of sleep health score with 3 sleep behaviors (including insomnia, snoring, dozing), 4 psychiatry disorders (major depressive disorder, attention deficit/hyperactivity disorder, schizophrenia, autism spectrum disorder), and 9 plasma protein (such as Stabilin-1, Stromelysin-2, Cytochrome c) (all LDSC PLDSC < 0.05).ConclusionOur results advance the comprehensive understanding of the aetiology and genetic architecture of the sleep health score, refine the understanding of the relationship of sleep health score with other traits and diseases, and may serve as potential targets for future mechanistic studies of sleep phenotype.

New Etiological Insights of Psychiatric Disorders by Proteome-wide Association Study

In the current issue of Biological Psychiatry, Liu et al. (1) provide important insights into the etiological component of protein abundance in psychiatric disorders, which will help to further the mechanistic investigation and development of potential novel therapeutic targets.

Identification of novel drug targets for Alzheimer's disease by integrating genetics and proteomes from brain and blood.

Genome-wide association studies (GWASs) have discovered numerous risk genes for Alzheimer's disease (AD), but how these genes confer AD risk is challenging to decipher. To efficiently transform genetic associations into drug targets for AD, we employed an integrative analytical pipeline using proteomes in the brain and blood by systematically applying proteome-wide association study (PWAS), Mendelian randomization (MR) and Bayesian colocalization. Collectively, we identified the brain protein abundance of 7 genes (ACE, ICA1L, TOM1L2, SNX32, EPHX2, CTSH, and RTFDC1) are causal in AD (P < 0.05/proteins identified for PWAS and MR; PPH4 >80% for Bayesian colocalization). The proteins encoded by these genes were mainly expressed on the surface of glutamatergic neurons and astrocytes. Of them, ACE with its protein abundance was also identified in significant association with AD on the blood-based studies and showed significance at the transcriptomic level. SNX32 was also found to be associated with AD at the blood transcriptomic level. Collectively, our current study results on genetic, proteomic, and transcriptomic approaches has identified compelling genes, which may provide important leads to design future functional studies and potential drug targets for AD.

Proteome-wide Association Study Provides Insights Into the Genetic Component of Protein Abundance in Psychiatric Disorders

BackgroundGenome-wide association studies have identified multiple risk variants for psychiatric disorders. Nevertheless, how the risk variants confer risk of psychiatric disorders remains largely unknown. MethodsWe performed proteome-wide association studies to identify genes whose cis-regulated protein abundance change in the human brain were associated with psychiatric disorders. ResultsBy integrating genome-wide associations of four common psychiatric disorders and two independent brain proteomes (n = 376 and n = 152, respectively) from the dorsolateral prefrontal cortex, we identified 61 genes (including 48 genes for schizophrenia, 12 genes for bipolar disorder, 5 genes for depression, and 2 genes for attention-deficit/hyperactivity disorder) whose genetically regulated protein abundance levels were associated with risk of psychiatric disorders. Comparison with transcriptome-wide association studies identified 18 overlapping genes that showed significant associations with psychiatric disorders at both proteome-wide and transcriptome-wide levels, suggesting that genetic risk variants likely confer risk of psychiatric disorders by regulating messenger RNA expression and protein abundance of these genes. ConclusionsOur study not only provides new insights into the genetic component of protein abundance in psychiatric disorders but also highlights several high-confidence risk proteins (including CNNM2 and CTNND1) for schizophrenia and depression. These high-confidence risk proteins represent promising therapeutic targets for future drug development.

Abstract 19: Proteome-wide biomarker discovery using digital MosaicNeedles

Abstract Changes in transcript levels detected by transcriptome profiling have been widely used as a surrogate for proteomics. However, recent studies have shown accumulating evidence that phenotypes of diseases that show correlations with protein levels cannot be explained by transcript levels. Hence a proteome-wide biomarker search will provide valuable information for cancer research that is not achievable from a strictly genomic approach. NanoMosaic is a digital immunoassay technology that uses a sub-100 nm nanoneedle sensor to detect single molecules. Over a billion nanoneedles are fabricated on a silicon substrate, thus named MosaicNeedlesTM, and formatted into 96, 384 or 1536 isolated well configurations at standard microplate dimensions. Each nanoneedle is a label-free biosensor, that is functionalized with capture antibodies. Each individual nanoneedle shifts its scattering spectrum when an antigen binds to it. The spectrum shift is further enhanced using a chemical amplification process. At low concentrations, the binding events of molecules to the MosaicNeedlesTM follow a Poisson distribution which insure that the number of molecules can be quantitated by counting the number of nanoneedles that display a spectrum change. Imaging automation is achieved by TessieTM instrument that reads the entire MosaicNeedlesTM and analyzes the color spectrum of individual nanoneedles. High multiplexing level up to 50 analytes can be achieved by parallelizing the detection in a microarray format within each well. NanoMosaic uses a hypothesis-free approach to construct its protein biomarker discovery panel, also known as proteome-wide association study (PWAS) panel. Individual protein analytes included in the PWAS panel are evenly spaced across the exome. Therefore, a sub-group of the entire human proteome or secretome can be interrogated in a hypothesis-free way before the entire proteome or a reflexive protein sub-group is interrogated. This approach addresses the limitations of the availability of the affinity reagents (antibodies, nanobodies, aptamers and etc.) that would be needed to simultaneously interrogate the whole proteome. Once valuable targets of interest are identified by running the PWAS-panel against a cohort of samples, mechanism studies can then be done using pathway specific panels whose design can be informed by previous PWAS results. In conclusion, NanoMosaic provides a solution to conduct a proteome-wide search for disease relevant biomarkers using its hypothesis-free PWAS-panels. The use of MosaicNeedlesTM provides higher sensitivity, wider dynamic range, lower cost and higher throughput than is currently possible by mass spectrometry or traditional immunoassay methods. Citation Format: Qimin Quan, Joshua Ritchey, Joe Wilkinson, John Geanacopoulos, Alaina Kaiser, John Boyce. Proteome-wide biomarker discovery using digital MosaicNeedles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 19.

Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk.

The plasma proteomic changes that precede the onset of dementia could yield insights into disease biology and highlight new biomarkers and avenues for intervention. We quantified 4,877 plasma proteins in nondemented older adults in the Atherosclerosis Risk in Communities cohort and performed a proteome-wide association study of dementia risk over five years (n = 4,110; 428 incident cases). Thirty-eight proteins were associated with incident dementia after Bonferroni correction. Of these, 16 were also associated with late-life dementia risk when measured in plasma collected nearly 20 years earlier, during mid-life. Two-sample Mendelian randomization causally implicated two dementia-associated proteins (SVEP1 and angiostatin) in Alzheimer's disease. SVEP1, an immunologically relevant cellular adhesion protein, was found to be part of larger dementia-associated protein networks, and circulating levels were associated with atrophy in brain regions vulnerable to Alzheimer's pathology. Pathway analyses for the broader set of dementia-associated proteins implicated immune, lipid, metabolic signaling and hemostasis pathways in dementia pathogenesis.

Brain proteome-wide association study implicates novel proteins in depression pathogenesis.

Depression is a common condition but current treatments for depression are only effective in a subset of individuals. To identify novel treatment targets, we integrated depression GWAS results (N=500,199) with human brain proteomes (N=376) to perform a proteome-wide association study (PWAS) of depression, followed by Mendelian randomization. We identified 19 genes consistent with being causal in depression, acting via their cis-regulated brain protein abundance. We replicated 9 of these genes using an independent depression GWAS (N=307,353) and human brain proteomic dataset (N=152). Eleven of these 19 genes also had their cis-regulated mRNA levels associated with depression based on integration of the depression GWAS with human brain transcriptomes (N=888). Meta-analysis of the discovery and replication PWAS identified 25 brain proteins consistent with being causal in depression, and 20 were not previously implicated in depression by GWAS. Together, these findings provide novel promising brain protein targets for further mechanistic and therapeutic studies.

Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis.

Genome-wide association studies (GWAS) have identified many risk loci for Alzheimer's disease (AD)1,2, but how these loci confer AD risk is unclear. Here, we aimed to identify loci that confer AD risk through their effects on brain protein abundance to provide new insights into AD pathogenesis. To that end, we integrated AD GWAS results with human brain proteomes to perform a proteome-wide association study (PWAS) of AD, followed by Mendelian randomization and colocalization analysis. We identified 11 genes that are consistent with being causal in AD, acting via their cis-regulated brain protein abundance. Nine replicated in a confirmation PWAS and eight represent new AD risk genes not identified before by AD GWAS. Furthermore, we demonstrated that our results were independent of APOE e4. Together, our findings provide new insights into AD pathogenesis and promising targets for further mechanistic and therapeutic studies.

PWAS: proteome-wide association study\u2014linking genes and phenotypes by functional variation in proteins

We introduce Proteome-Wide Association Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein’s function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.

Cortical Proteins Associated With Cognitive Resilience in Community-Dwelling Older Persons

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. To identify proteins associated with cognitive resilience via a proteome-wide association study of the human dorsolateral prefrontal cortex. 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. Participants had undergone annual detailed clinical examinations, postmortem evaluations, and tandem mass tag proteomics analyses. 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. 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. These protein signals may represent novel targets for the maintenance of cognition in old age.

Revisiting atherosclerosis and dementia.

Hardening of the arteries (atherosclerosis) was linked to dementia long ago, but subsequently, Alzheimer’s plaques and tangles have received more attention. A new proteome-wide association study unveils molecular links between intracranial atherosclerosis and dementia, independent of other pathologies, providing new evidence for one of the oldest suspected causes of dementia.

Human Brain Proteome-Wide Association Study of Depression Identified 24 Proteins as Tractable Targets for Further Mechanistic Studies

Human Brain Proteome-Wide Association Study of Depression Identified 24 Proteins as Tractable Targets for Further Mechanistic Studies