New Database May Help Diagnose and Predict Outcomes of Mitochondrial Diseases

A new tool to help in the diagnosis and outcome prediction of mitochondrial disease has been created by researchers from the U.S. and Finland, according to a study.

Findings from the study, “Pathogenicity in POLG syndromes: DNA polymerase gamma pathogenicity prediction server and database,” appeared in the journal BBA Clinical.

Mitochondrial disease is primarily caused by genetic mutations that result in loss of function of DNA polymerase gamma (POLG), the enzyme responsible for the integrity of mitochondrial DNA. This mutation results in dysfunctional mitochondria (the powerhouse of cells), which can affect multiple organs, motor function, and the nervous system.

The disease presents a wide clinical spectrum, which represents an important challenge to the diagnosis.

“POLG disorders, largely neurological and muscular, range from prenatally fatal conditions and severe infantile onset disorders, to milder, late onset conditions,” Laurie Kaguni, PhD, the study’s lead author, said in a press release.

Kaguni is a professor at Michigan State University (MSU) and director of the university’s Center for Mitochondrial Science and Medicine. The author added that most of the symptoms are complicated by the genetic complexity of the disease.

“That presents a huge problem for pathogenicity prediction, and one that we decided to tackle,” she said.

The work was done in collaboration with a team headed by Professor Anu Suomalainen, an MD and PhD at the University of Helsinki in Finland.

The study focused on Alpers syndrome, a severe form of POLG syndrome with early onset that frequently causes death by age 2. Alpers syndrome presents nearly unmistakable symptoms such as epilepsy, loss of brain function, and liver failure that facilitates research and enables the use of its 67 known gene mutations rather than all 176 POLG mutations.

The researchers created a new tool — the POLG Pathogenicity Prediction Server — which revealed that the gene mutations can be grouped in five distinct clusters (groups). When adding the remaining POLG mutations to the analysis, the scientists observed that all but two of them fell within the same five groups. This result allows for the prediction of clinical outcomes in cases where patients have mutations from different clusters.

“These findings show us that we can predict — for any given mutation — what impact it will have on the biochemistry of the enzyme,” Kaguni said.

The discovery also enables clinicians to “predict with reasonable confidence whether the disease is going to be early, mid-life or later-life onset — and what the symptoms are likely to be,” she added.

The database contains anonymous information on 681 POLG patients, including their age of diagnosis and symptoms.

The server assigns new cases to the clusters and shows any similar existing patient data. It also indicates the most probable age of onset, which will serve as the basis for a diagnosis and prognosis for the patient.

The new tool takes advantage of the significant number of mutations in the general population, which helps it gather information on symptoms and predict disease outcome. “If someone has been diagnosed with a particular mutant pair and there is published data on it, you can find out quite accurately what is likely to happen,” Kaguni said.

The scientists aim to use the new database to enable early diagnosis, which could improve outcomes in cases that are expected to be developmentally lethal. It could also help develop better diet and physical therapy regimens for late-onset disorders.