Genetic and Transcriptomic Biomarkers: History
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Subjects: Clinical Neurology
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Julie Lake

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. 

  • Alzheimer’s disease
  • Parkinson’s disease

1. Introduction

In 1998, the National Institutes of Health’s Biomarkers Definitions Working Group defined a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” [1]. It is widely assumed that a successful biomarker must be objective, inexpensive, accessible, accurate in a diverse group of individuals and easily quantifiable, and correlate with the presence or severity of the disease [2].

In the neurodegenerative diseases field, the discovery and validation of biomarkers is an area of ongoing effort and interest. A plethora of studies have been conducted in an attempt to unravel biomarkers that may be characteristic indicators for preclinical disease diagnosis (before clinical symptoms occur), predictive prognosis, and disease subtyping. In this arena, the search may be particularly difficult because these conditions are not clearly defined entities. They are etiologically and clinically heterogeneous, and they may rather reflect a spectrum of neurodegenerative disease processing. The intra- and inter-patient variation and the fact that co-pathologies are frequent and have complex contributions to clinical phenotypes makes biomarker discovery particularly challenging.

Over the years, biomarker studies conducted in the field of neurogenetics have usually focused on identifying single biomarker metrics with limited applicability (Table 1).

Table 1. Genetic biomarkers for neurodegenerative diseases.

Gene Protein Neurodegenerative Disease
SNCA Alpha-synuclein monogenic PD
LRRK2 Leucine-Rich Repeat Kinase 2 monogenic PD
PINK2 PTEN-induced kinase 1 monogenic PD
PARK2 Parkin monogenic PD
DJ-1 DJ-1 monogenic PD
VPS35 Vacuolar protein sorting ortholog 35 monogenic PD
GBA Glucocerebrosidase PD (risk factor)
APP Amyloid precursor protein monogenic AD
PSEN1 Presenilin 1 monogenic AD
PSEN2 Presenilin 2 monogenic AD
APOE (ε4 allele) Apolipoprotein-E AD (risk factor)
TREM2 TREM2 AD (risk factor)
TARDBP TDP-43 monogenic ALS
SOD1 Superoxide dismutase 1 monogenic ALS
FUS Fused-in sarcoma monogenic ALS
C9orf72 C9orf72 ALS (risk factor)
KIF5A KIF5A ALS (risk factor)

These genetic markers are often disease-causing deleterious mutations responsible for monogenic forms of disease. However, even in the majority of the cases, the relationship between a genetic biomarker and the development of the disease is complex, due to the variability of penetrance and the contribution of genetic risk factors interplaying with the environment.

The underlying molecular complexity in neurodegenerative diseases has made the next generation of biomarker studies take shape as meaningful multi-modal approaches using large scale biobank datasets. To a large extent, our current knowledge about the etiology underlying neurodegenerative diseases has been driven by advances in the known ”-omics”, including genomics, transcriptomics, proteomics, and metabolomics. Despite being widely applied in research, the road towards a successful implementation and translation into the clinic is in its early stages. The availability of reliable biomarkers able to provide an early diagnosis and the identification of individuals at risk, monitor disease progression and allow the discovery of novel and more individualized treatments for these debilitating conditions is urgently needed in our search for a cure.

2. Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative movement disorder in which the diagnosis is currently based on the patient’s clinical history and examination. The clinical diagnosis at first visit is, however, only accurate in 80% of pathologically-confirmed PD [3]. The classical presentation includes progressive slow movements, resting tremor, and stiffness [4], and patients often report long-standing, prodromal non-motor symptoms [4]. Dopamine transporter imaging can be helpful for diagnosis when the examination does not clearly reveal parkinsonism, but its usefulness is limited when parkinsonian motor signs are unequivocally present [4]. There has been extensive research searching for protein biomarkers in cerebrospinal fluid and blood [5], but these findings have not yet been translated to the clinic. As such, there remains an unmet need for objective biomarkers for early-stage diagnosis [6].

2.1. Genetic Biomarkers

2.1.1. Rare Mutations

While the cause of PD is unclear, there are several genetic and environmental risk factors. The genetic contributors to PD risk lie on a spectrum from rare variants with strong effects to common variants with weak effects. A minority of PD cases carry rare mutations that are sufficient to cause a familial or monogenic form of neurodegeneration, reviewed in references [7][8][9]. These include mutations and/or copy number variants in SNCA, LRRK2, PINK1, PARK2, DJ1, or VPS35. While these mutations can be considered relatively reliable biomarkers for some patients, the vast majority of PD cases do not have a clear genetic cause. As such, genetic variation is usually considered a risk factor for this disease. For example, mutations in the GBA gene have been linked to roughly a fivefold increase in PD risk [10]. As previously mentioned, the clinical usefulness of these mutations is limited by their low prevalence and incomplete penetrance [11][12].

2.1.2. Common Variants and Polygenic Risk Scores

Genome-wide association studies (GWASs) have identified over 90 common genetic variants associated with PD risk in Europeans, and 11 in Asian populations [13][14]. While each GWAS-identified variant accounts for a very small proportion of this risk, variants can be aggregated to form a polygenic risk score (PRS). Using the effect sizes and alleles calculated for each variant in the GWAS, a PRS could be used as a biomarker to estimate an individual’s risk of disease. Several studies have shown that GWAS-derived PRSs correlate with disease risk, age at onset, as well as motor and cognitive decline (measured by change in UPDRS part III score, time to Hoehn and Yahr stage 3, change in the mini-mental state examination), but not survival [15][16][17][18][19].

Nevertheless, genetic testing does not currently have an established role in the diagnostic process unless the patient’s history prompts suspicion for a genetic cause through, for example, a family history or early motor symptom onset. Calculating an individual’s PRS would need to have a substantial impact on clinical trial recruitment or patient quality of life before it could be implemented. Genetic variation is estimated to account for about 22% of PD risk, and to date only 16–36% of that risk may be explained by GWAS-identified loci (depending on the estimated disease prevalence) [13]. It is thus unlikely that such a PRS alone could currently have a substantial impact on patient care. Furthermore, the vast majority of GWAS data is based on individuals of European descent only. The less an individual is genetically similar to the GWAS study population, the less accurate the PRS will be in predicting disease risk in that individual [20][21][22]. As such, current PRSs do not yet reach the diagnostic accuracy needed to be translated to the clinic.

2.2. Transcriptomic Biomarkers

Beyond genetics, the potential of RNA-based biomarkers have recently been explored in PD research (Table 2).

Table 2. Potential transcriptomic biomarkers in neurodegenerative diseases.

Gene Tissue/Biofluid Upregulated/Downregulated Neurodegenerative Disease
miRNA-153 Saliva Downregulated Sporadic PD
miRNA-223 Saliva Downregulated Sporadic PD
MAPK9_circ_0001566 PBMCs Downregulated Sporadic PD
HOMER1_circ_ 0006916 PBMCs Downregulated Sporadic PD
SLAIN1_circ_0000497 PBMCs Downregulated Sporadic PD
DOP1B_circ_0001187 PBMCs Downregulated Sporadic PD
RESP1_circ_0004368 PBMCs Downregulated Sporadic PD
PSEN1_circ_0003848 PBMCs Downregulated Sporadic PD
miR-7-5p Plasma Upregulated Sporadic PD
miR-22-3p Plasma Upregulated Sporadic PD
miR-124-3p Plasma Upregulated Sporadic PD
miR-136-3p Plasma Upregulated Sporadic PD
miR-139-5p Plasma Upregulated Sporadic PD
miR-330-5p Plasma Upregulated Sporadic PD
miR-433-3p Plasma Upregulated Sporadic PD
miR-495-3p Plasma Upregulated Sporadic PD
APOE CNS Upregulated Sporadic AD
TREM2 CNS Upregulated AD
APP; β-amyloid protein (Aβ42/Aβ40) CSF; Blood/Plasma Upregulated Familial AD
MAPT (Phosphorylated tau 181 or 231) CSF; Blood/Plasma Upregulated Sporadic AD
MAPT (Total tau) CSF; Blood/Plasma Upregulated Sporadic AD
NEFL (NfL; neurofilament light chain) CSF; Blood/Plasma Upregulated Sporadic AD
GFAP (Glial fibrillary acidic protein) Blood/Plasma Upregulated AD
miR-101   Downregulated AD
miR-153   Downregulated AD
miR-346   Upregulated AD
miR-342-3p Blood/Plasma Upregulated AD
miR-455-3p CNS; Serum Upregulated AD
miR-146a CSF Upregulated AD
miR-34a-5p CNS; Serum Upregulated AD
miR-93 Serum Downregulated AD
miR-127-3p CSF Downregulated AD
KIF5C CNS, PBMCs Downregulated Sporadic ALS
KIFC3 CNS, PBMCs Downregulated Sporadic ALS
DCTN1 CNS, PBMCs Inconsistent results Sporadic ALS
Trk-B PBL Downregulated ALS (non-specific)
BDNF PBL Downregulated ALS (non-specific)
PI3K PBL Downregulated ALS (non-specific)
AKT PBL Downregulated ALS (non-specific)
NFκB PBL Downregulated ALS (non-specific)
GSK3β PBL Downregulated ALS (non-specific)
FASL PBL Upregulated ALS
CyFIP2 hMSC, PBL Upregulated Sporadic ALS
RbBP9 hMSC, PBL Upregulated Sporadic ALS
VEGF-A PBMCs Upregulated Sporadic ALS
CCL2 PBMCs Upregulated Sporadic ALS
Nurr1 Whole blood Upregulated ALS
COL19A1 Whole blood Upregulated ALS (prognosis)
miR-1234-3p Serum, Plasma Downregulated Sporadic ALS
miR-1825 Serum, Plasma Downregulated ALS
miR-206 Serum, Plasma, PBL Upregulated Sporadic ALS (non-specific)
miR-338-3p PBL, Serum, CSF Upregulated Sporadic ALS (non-specific)
miR-9 Plasma, CSF, PBL Upregulated Sporadic ALS (non-specific)

The current table represents some of the examples discussed in the text. It does not include by any means a complete list of the numerous differently expressed genes that have been associated with neurodegenerative conditions in the extensive literature. CNS = central nervous system, PBL = peripheral blood leukocytes, hMSC = human mesenchymal stem cells, PBMC = peripheral blood.

Several studies have sought to classify gene expression profiles in PD for diagnostic purposes [23][24], and three forms of non-coding RNAs have been investigated as potential biomarkers for PD: microRNA (miRNA), long non-coding RNA (lncRNA) and circular RNA (circRNA) [25][26][27][28].

miRNAs are short RNA molecules that are easily detected in body fluids such as blood, CSF, or saliva. Many studies have compared expression levels of various miRNAs between PD patients and healthy controls [25][26]. For example, Cressatti et al., found that salivary miRNA-153 and miRNA-223 may be able to distinguish PD patients from controls with an area under the curve of 79% (95% confidence interval (CI), 64.5–99.2) and 74% (95% CI, 59.6–93.0), respectively [29]. Similarly, Ravanidis and colleagues identified six circRNAs that may be deregulated in PD patients [30]. The authors combined four of these into one biomarker, which in the same patients had a sensitivity of 75.3%, a specificity of 78%, and an area under the curve of 0.84. It has been suggested that biomarkers should achieve areas under the curve >80% in order to be clinically useful [31]. While current miRNA studies are encouraging, the diagnostic accuracy of a biomarker must be measured in a cohort that is independent of the discovery population.

Furthermore, RNA-based biomarker studies in PD have focused on discerning PD patients with motor symptoms from healthy controls. In the clinic, the difficulty often lies in distinguishing idiopathic PD from other causes of parkinsonian symptoms such as progressive supranuclear palsy, multiple system atrophy, or monogenic PD. In this vein, a recent miRNA study identified dysregulated miRNAs that differed between patients with idiopathic vs. monogenic forms of PD, and they found some overlap between patients carrying SNCA and GBA mutations [32]. From a diagnostic point of view, a biomarker distinguishing monogenic and sporadic PD could help identify cases caused by de novo mutations.

Establishing reproducible, robust RNA-based biomarkers for PD has been a great challenge, in part because most studies have very small sample sizes and the techniques used to detect and analyze miRNA levels are not standardized [25][26][33]. A recent review found that the sensitivity among 24 miRNA studies looking to distinguish between PD cases and healthy controls ranged from 56.7% to 96%, and their specificity from 63.3 to 92% [26]. As such, thorough replication studies will be crucial before these biomarkers can be considered in the clinic.

An early diagnostic test or a progression biomarker would allow pre-symptomatic or high-risk individuals to make more informed plans for their future and, thus, improve quality of life [34]. Such tools would also enable clinical-phase research to target pathogenic processes at an early stage. The underlying disease-causing process of PD is thought to occur up to two decades before motor symptom onset, suggesting that there is indeed a pathological process to be detected early on [35][36]. Longitudinal, population-based biomarker studies will therefore be crucial for establishing clinically effective biomarkers in PD.

This entry is adapted from the peer-reviewed paper 10.3390/cells10051030

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