Two blood biomarkers may help predict risk of Parkinson’s: Study
Pair emerged from among a gene signature of 9 disease biomarkers
Scientists have identified a nine-gene signature model to predict the risk of Parkinson’s disease by integrating data from blood and brain tissue gene expression studies.
The blood levels of two genes in particular — PLOD3 and LRRN3 — stood out as potential biomarkers to diagnose the disease in its early stages.
The “combined detection of PLOD3 and LRRN3 expression in blood samples can improve the early detection of” Parkinson’s, the researchers wrote in “Identification of PLOD3 and LRRN3 as potential biomarkers for Parkinson’s disease based on integrative analysis,” which was published in npj Parkinson’s Disease.
Parkinson’s is commonly diagnosed after its symptoms appear, perhaps years after the underlying neurodegeneration has begun when potential treatments are less effective. For this reason, identifying biomarkers that can aid in detecting the disease early is the key to treating it.
A team led by researchers at Shandong University, China analyzed integrated transcriptome data — the entire set of messenger RNA molecules that give rise to proteins — from postmortem brain and blood samples from Parkinson’s patients and people without the disease, who served as controls.
They specifically analyzed transcriptome data from six studies on brain samples from the substantia nigra, which is involved in controlling voluntary movements and one of the most affected in Parkinson’s.
Expression of PLOD3, LRRN3 as biomarkers of Parkinson’s
The analysis revealed 921 genes in brain tissue and 1,001 genes in the blood with a significantly different expression than controls. Gene expression is when information in a gene is synthesized to create a working product, like a protein.
Researchers then used an algorithm, called weighted gene co-expression network analysis (WGCNA), to identify how many of these genes correlated with Braak staging, a measure of disease progression that correlates with developing clinical symptoms such as bradykinesia (slowness or difficulty in movement), rigidity, or cognitive decline. Combining the brain and blood analysis led to 58 genes likely playing a role in Parkinson’s.
The researchers then ran the 58 genes through another algorithm, called least absolute shrinkage and selection operator regression (LASSO), and combined it with another statistical analysis that let them create a Parkinson’s gene signature of nine biomarker genes – BHD2, BASP1, CTBP2, GCM1, GMPR2, GPX3, LRRN3, PLOD3, and RBM38, which could predict the risk of Parkinson’s incidence with a higher degree of accuracy. Compared to controls, the risk scores of Parkinson’s patients were significantly higher.
Of the nine, only PLOD3 and LRRN3 were significantly linked with Braak stages. The levels of PLOD3 were elevated, while those of LRRN3 were reduced in patients’ brain and blood samples.
According to researchers, “PLOD3 and LRRN3 are not only implicated in the diagnostic significance of PD [Parkinson’s disease],” but might play a role in the mechanisms underlying the disease. They suggest PLOD3 is implicated in activating the immune response and inflammatory cells infiltrating the brain, while LRRN3 is associated with suppressing immune responses.
While a feature of Parkinson’s is the death and dysfunction of dopamine-producing neurons in the brain, other brain cells are known to play a role.
To understand the expression profiles of PLOD3 and LRRN3 in different brain cells, the researchers used data from the transcriptome of each individual cell in the substantia nigra.
They found that microglia, the immune cells of the brain, had higher levels of PLOD3, while LRRN3 expression was more enriched in neurons and oligodendrocyte progenitor cells (OPCs), immature cells that will turn into mature, myelin-making oligodendrocytes. Myelin is the fat-rich substance that wraps around nerve fibers (axons) and works to insulate and increase the velocity of the signals relayed by nerve cells.
The PLOD3 gene was involved in suppressing cellular metabolic functions and inflammatory cell infiltration, while LRRN3 exhibited opposite effects, a computational analysis to assess the pathways the genes might regulate suggested.
Measuring PLOD3, LRRN3 in different patient group
To validate the findings, the team measured the expression levels of PLOD3 and LRRN3 in blood samples from a new group of 35 Parkinson’s patients and 23 controls. Similar to their previous findings, expression levels of PLOD3 were greatly elevated in patients over controls, while the opposite was seen with LRRN3.
To further assess the predictive value of each gene, the researchers analyzed their expression in blood samples of patients treated with levodopa, or L-DOPA, a precursor to dopamine, the brain chemical missing in Parkinson’s, and one of the gold standards of treatment.
Those with better L-DOPA responses, as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS), had lower PLOD3 and higher LRRN3 expression, further showing “the significance of examining PLOD3 and LRRN3″ for diagnosing Parkinson’s in blood samples.
“In the future, the potential role and mechanism of PLOD3 and LRRN3 in regulating immune infiltration during the progression of PD [Parkinson’s disease] could be focused on research,” the researchers said.
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