Metabolomics meets genomics to improve patient diagnosis

Date:
July 7, 2020
Source:
Baylor College of Medicine
Summary:
Researchers have improved their ability to identify the genetic
cause of undiagnosed conditions.



FULL STORY ==========================================================================
A patient and family walk into a doctor's office. They hope that the
latest tests will reveal what is causing the patient's illness and end
the diagnostic odyssey they have been going through for years. Having
an accurate diagnosis also means that maybe there is a treatment that
at least can alleviate the patient's condition.


==========================================================================
At Baylor College of Medicine, Dr. Sarah Elsea and her colleagues have
been working on improving their ability to identify the genetic cause
of undiagnosed conditions. Their study appears in the journal Genetics
in Medicine.

"About nine of every 10 patients that are referred to us have neurological conditions, such as developmental delay and intellectual disability, for
which they don't have a diagnosis," said Elsea, professor of molecular
and human genetics at Baylor and corresponding author of the work.

To identify the genetic cause of undiagnosed conditions, the researchers
look for potentially defective genes in the patient's genome. They
use whole-exome sequencing, which analyzes all the genes that encode
proteins. A gene may have many variants that encode slightly different
versions of the same protein that still carry their function normally. But
some variants may encode defective proteins that can cause disease. The challenging part is determining whether the variant of a particular gene
that is found in a patient is causing the disease.

"In some cases, the variant is missing all or a large portion of the
gene, which results in a non-functional protein. This suggests that the
variant is involved in the disease. However, most genetic variants involve changes in a single building block of the DNA. That one 'misspelled'
gene sequence may or may not result in a defective or less functional
protein, and we need other mechanisms, such as untargeted metabolomics,
to determine if that genetic change causes disease," said Elsea, who
also is the senior director of biochemical genetics at Baylor Genetics.

Enter untargeted metabolomics Elsea and her colleagues used untargeted metabolomics to provide an additional level of information to help them determine whether the genetic variant they found in the patient was
actually causing the condition.



========================================================================== "Untargeted metabolomics lets us look at the function of the protein
encoded by the gene variant in the patient to explore metabolic
abnormalities that may be associated with the variant," Elsea said.

In the current study, the researchers integrated whole-exome sequencing
and targeted metabolomics to analyze the data of a group of 170
patients. They were pleased to find that the metabolomics data informed
44 percent of the cases.

"The analysis let us reclassify nine variants as likely benign,
15 variants as likely causing disease and three as disease-causing
variants. Metabolomics data confirmed a clinical diagnosis in 21 cases,"
Elsea said. "Our analysis is extremely helpful not only for confirming
that a variant causes the condition, but also to rule out variants as the
cause of disease. Having a more accurate diagnosis may help identify a
better treatment for the condition and also provides important information
for the family regarding recurrence risk." This analysis also aids
with the diagnosis of patients that may have a mild form of a disease,
because the analysis is broad and very sensitive and shows the effects
of the variant in entire metabolic pathways.

"We have been able to identify a few cases with milder diseases. Before
our integrated analysis, we would not have diagnosed those cases with
the disease, but we can now because metabolomics revealed metabolic abnormalities that we could link to the gene variant in the patient,"
Elsea said. "This approach has improved diagnostics substantially and
also increased our understanding of these conditions and the range of
clinical manifestations that we might see in patients." The researchers
hope that their integrated multi-omics analysis will help other patients
by providing a diagnosis, clarifying previous suspected diagnoses or
monitoring their treatment.


========================================================================== Story Source: Materials provided by Baylor_College_of_Medicine. Original written by Ana Mari'a Rodri'guez, Ph.D.. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Joseph T. Alaimo, Kevin E. Glinton, Ning Liu, Jing Xiao, Yaping
Yang, V.

Reid Sutton, Sarah H. Elsea. Integrated analysis of
metabolomic profiling and exome data supplements sequence variant
interpretation, classification, and diagnosis. Genetics in Medicine,
2020; DOI: 10.1038/ s41436-020-0827-0 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200707120653.htm

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