Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome

Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer's disease

The integration of quantitative trait loci (QTL) with disease genome-wide association studies (GWAS) has proven successful at prioritizing candidate genes at disease-associated loci. QTL mapping has mainly been focused on multi-tissue expression QTL or plasma protein QTL (pQTL). Here we generated the largest-to-date cerebrospinal fluid (CSF) pQTL atlas by analyzing 7,028 proteins in 3,107 samples. We identified 3,373 independent study-wide associations for 1,961 proteins, including 2,448 novel pQTLs of which 1,585 are unique to CSF, demonstrating unique genetic regulation of the CSF proteome. In addition to the established chr6p22.2-21.32 HLA region, we identified pleiotropic regions on chr3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron-specificity and neurological development. We also integrated this pQTL atlas with the latest Alzheimer's disease (AD) GWAS through PWAS, colocalization and Mendelian Randomization and identified 42 putative causal proteins for AD, 15 of which have drugs available. Finally, we developed a proteomics-based risk score for AD that outperforms genetics-based polygenic risk scores. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility

Identification of protein quantitative trait loci (pQTL) helps understand the underlying mechanisms of diseases and discover promising targets for pharmacological intervention. For most important class of drug targets, genetic evidence needs to be generalizable to diverse populations. Given that the majority of the previous studies were conducted in European ancestry populations, little is known about the protein-associated genetic variants in East Asians. Based on data-independent acquisition mass spectrometry technique, we conduct genome-wide association analyses for 304 unique proteins in 2,958 Han Chinese participants. We identify 195 genetic variant-protein associations. Colocalization and Mendelian randomization analyses highlight 60 gene-protein-phenotype associations, 45 of which (75%) have not been prioritized in Europeans previously. Further cross-ancestry analyses uncover key proteins that contributed to the differences in the obesity-induced diabetes and coronary artery disease susceptibility. These findings provide novel druggable proteins as well as a unique resource for the trans-ancestry evaluation of protein-targeted drug discovery.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

Mapping the serum proteome to neurological diseases using whole genome sequencing

Despite the increasing global burden of neurological disorders, there is a lack of effective diagnostic and therapeutic biomarkers. Proteins are often dysregulated in disease and have a strong genetic component. Here, we carry out a protein quantitative trait locus analysis of 184 neurologically-relevant proteins, using whole genome sequencing data from two isolated population-based cohorts (N = 2893). In doing so, we elucidate the genetic landscape of the circulating proteome and its connection to neurological disorders. We detect 214 independently-associated variants for 107 proteins, the majority of which (76%) are cis-acting, including 114 variants that have not been previously identified. Using two-sample Mendelian randomisation, we identify causal associations between serum CD33 and Alzheimer's disease, GPNMB and Parkinson's disease, and MSR1 and schizophrenia, describing their clinical potential and highlighting drug repurposing opportunities.

Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer's disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer's disease, Parkinson's disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Association between herpes simplex virus 1 exposure and the risk of depression in UK Biobank

BACKGROUND

Herpes simplex virus-1 (HSV-1) infection is reported to be associated with depression. But limited efforts were made to investigate the relationship between HSV-1 infection and the risk of depression, especially from the genetic perspective.

METHODS

In UK Biobank cohort, linear and logistic regression analyses were first performed to test the association of HSV-1 seropositivity/antibody with depression, including depression status (N = 2951) and Patient Health Questionnaire (PHQ) score (N = 2839). Using individual genotypic and phenotypic data from the UK Biobank, genome-wide environmental interaction study (GWEIS) was then conducted by PLINK2.0 to evaluate gene × HSV-1 interacting effect on the risk of depression. Finally, gene set enrichment analysis was conducted to identify the biological pathways involved in the observed gene × HSV-1 interaction for depression.

RESULT

In UK Biobank cohort, significant associations were observed between depression status and HSV-1 (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.02-1.16; P = 2.40 × 10^-2 for HSV-1 antibody and OR = 1.28; 95% CI, 1.12-1.47, P = 2.59 × 10^-3 for HSV-1 seropositivity). GWEIS revealed four significant gene × HSV-1 interaction signals for PHQ score (all P < 5.0 × 10^-8 ) and the leading loci was SULF2 (rs6094791, P = 8.60 × 10^-9 ). Pathway analyses identified 21 pathways for PHQ score and 19 for depression status, including multiple neural development- and immune-related ones, such as KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION (false discovery rate [FDR] = 3.18 × 10^-2 ) for depression and LU_AGING_BRAIN_UP (FDR = 4.21 × 10^-2 ) for PHQ score.

CONCLUSION

Our results suggested that HSV-1 was associated with the risk of depression, which was modulated by the several genes that were related to the nerve development or immune function.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BACKGROUND

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.

RESULTS

In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.

CONCLUSIONS

PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Increased apolipoprotein E and decreased TNF-α in the cerebrospinal fluid of nondemented APOE-ε4 carriers

Aim

The ε4 allele of apolipoprotein E gene (APOE) is a well‐known risk factor of late‐onset Alzheimer's disease. However, little is known why this variant confers a risk for Alzheimer's disease. The aim of this study was to examine the influence of the APOE genotype on cerebrospinal fluid (CSF) protein levels.

Methods

The present study performed a secondary analysis on our previously generated database to compare the CSF levels of 1128 proteins between APOE‐ε4 carriers (28 subjects) and noncarriers (104 subjects). All subjects were physically healthy Japanese individuals without dementia.

Results

CSF levels of apoE2, apoE3, and apoE4 were significantly higher (all nominal P < 10 × 10⁻⁵, false discovery rate < 0.001) and those of tumor necrosis factor‐α (TNF‐α) were significantly lower (nominal P = 1.39 × 10⁻⁶, false discovery rate < 0.001) in APOE‐ε4 carriers than in noncarriers. No significant correlation was observed between the CSF levels of TNF‐α and any of the apoE proteins.

Conclusions

Our findings indicate the possible roles of apoE and TNF‐α in the pathogenesis of APOE‐ε4‐associated Alzheimer's disease.

The CSF levels of apoE2, apoE3, and apoE4 were higher in APOE‐ε4 carriers. The CSF levels of TNF‐α were significantly lower in APOE‐ε4 carriers. Our findings indicate the possible roles of apoE and TNF‐α in the pathogenesis of APOE‐ε4‐associated Alzheimer's disease.

Systemic factors as mediators of brain homeostasis, ageing and neurodegeneration

A rapidly ageing population and a limited therapeutic toolbox urgently necessitate new approaches to treat neurodegenerative diseases. Brain ageing, the key risk factor for neurodegeneration, involves complex cellular and molecular processes that eventually result in cognitive decline. Although cell-intrinsic defects in neurons and glia may partially explain this decline, cell-extrinsic changes in the systemic environment, mediated by blood, have recently been shown to contribute to brain dysfunction with age. Here, we review the current understanding of how systemic factors mediate brain ageing, how these factors are regulated and how we can translate these findings into therapies for neurodegenerative diseases.

Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936

Although plasma proteins may serve as markers of neurological disease risk, the molecular mechanisms responsible for inter-individual variation in plasma protein levels are poorly understood. Therefore, we conduct genome- and epigenome-wide association studies on the levels of 92 neurological proteins to identify genetic and epigenetic loci associated with their plasma concentrations (n = 750 healthy older adults). We identify 41 independent genome-wide significant (P < 5.4 × 10^-10) loci for 33 proteins and 26 epigenome-wide significant (P < 3.9 × 10^-10) sites associated with the levels of 9 proteins. Using this information, we identify biological pathways in which putative neurological biomarkers are implicated (neurological, immunological and extracellular matrix metabolic pathways). We also observe causal relationships (by Mendelian randomisation analysis) between changes in gene expression (DRAXIN, MDGA1 and KYNU), or DNA methylation profiles (MATN3, MDGA1 and NEP), and altered plasma protein levels. Together, this may help inform causal relationships between biomarkers and neurological diseases.

Trait Loci Mapping and CSF Proteome

Recent advance in high-throughput proteome analysis has enabled genome-wide and proteome-wide analyses of associations between single nucleotide polymorphisms and protein expression levels. Protein quantitative trait locus (pQTL) studies using cerebrospinal fluid (CSF) and DNA samples may provide valuable insights into the genetic basis and molecular mechanisms regulating protein expression in the central nervous system. In this chapter, we describe a step-by-step procedures of CSF collection and pQTL analysis, by using PLINK and R software.

Possible Influences of Endogenous and Exogenous Ligands on the Evolution of Human Siglecs

Sialic acids, a group of acidic sugars abundantly expressed in the tissues of deuterostome animals but rarely found in microbes, serve as a "signature of self" for these animals. Cognate sensors for sialic acids include Siglecs, a family of transmembrane lectins of vertebrate immune systems that recognize glycans containing sialic acids. A type of sialic acid called N-glycolylneuraminic acid (Neu5Gc) is abundant in many mammalian lineages including great apes, the closest extant relatives of modern human, but was lost in the lineage leading to modern human via the pseudogenization of the CMAH gene encoding the enzyme that converts N-acetylneuraminic acid (Neu5Ac) to Neu5Gc. Loss of Neu5Gc appears to have influenced the evolution of human Siglecs, such as the adjustment of sialic acid binding preferences and the inactivation of at least one Siglec. In addition, various mechanistic studies using model systems and genetic association studies have revealed that some human Siglecs interact with pathogens and influence the outcome of infections, and these pathogens in turn likely influence the evolution of these Siglecs. By understanding the evolutionary forces affecting Siglecs, we shall achieve a better appreciation of Siglec functions, and by understanding Siglec functions, we can obtain deeper insight into the evolutionary processes driving Siglec evolution.

A genome-wide association study on photic sneeze syndrome in a Japanese population

Photic sneeze syndrome (PSS) is characterized by a tendency to sneeze when the eye is exposed to bright light. Recent genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) associated with PSS in Caucasian populations. We performed a GWAS on PSS in Japanese individuals who responded to a web-based survey and provided saliva samples. After quality control, genotype data of 210,086 SNPs in 11,409 individuals were analyzed. The overall prevalence of PSS was 3.2%. Consistent with previous reports, SNPs at 3p12.1 were associated with PSS at genome-wide significance (p < 5.0 × 10^-8). Furthermore, two novel loci at 9q34.2 and 4q35.2 reached suggestive significance (p < 5.0 × 10^-6). Our data also provided evidence supporting the two additional SNPs on 2q22.3 and 9q33.2 reportedly associated with PSS. Our study reproduced previous findings in Caucasian populations and further suggested novel PSS loci in the Japanese population.