This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Medical researchers, clinicians and pharmaceutical scientists often study small molecules called metabolites, which are produced in living cells through a variety of processes, including the breakdown of food, chemicals or drugs. This area of study is known as metabolomics, and it can be used to identify new signs of disease, measure the effectiveness of medical treatment or study how diet and nutrition affect the body.
Many of these molecules can be identified through mass spectrometry, a lab test that measures the weight and charge of tiny particles, but the vast majority remain unknown. These unidentified particles are referred to as "dark matter," or the "dark metabolome." Although detectable, dark matter can't be matched to known molecular structures. These unknown particles leave behind a mass of data that is inaccessible for biological interpretation, creating a gap in knowledge of the genetic biome, cellular health, diseases and drug response.
But a new tool, the Molecular Community Network (MCN), developed by professor Vladimir Boginski and an interdisciplinary team of researchers, could illuminate the identity of molecules in the "dark matter." Their work has been published in Cell Reports Methods. "There are roughly 8.4 million observed mass spectra of known and unknown molecules combined in public repositories, and it is estimated that the dark matter comprises up to 90% of the entire observed molecular space," Boginski says. "Thus, it is crucial to develop methods that would allow one to systematically investigate this vast molecular space and potentially discover new molecules." Traditional molecular networking does not resolve the problem.
It connects molecules only when their similarity scores, calculated from observed mass spectra, exceed a predetermined threshold. This means biologically related molecules that fall below the cutoff can be separated from one another, fragmenting molecular families and limiting opportunities for discovery. Unlike existing networking methods, the MCN uses an algorithm that divides the entire molecular network into natural communities with many strong links within groups rather than between them.
It then retains the strongest connections to keep the communities connected. Rather than breaking molecular families into pieces, this process reveals the data structure that's already present. "This approach allows us to have almost every molecule in the network linked to at least one neighbor," Boginski says.
"Moreover, these links are typically between molecules from similar molecular families." This, in turn, facilitates annotation propagation, or the prediction of the identity of unknown molecules by looking at their known neighbors within a network community. "Since nearly 95% of molecules are now connected and assigned to network communities, we now have a much wider and richer search space for molecular discovery," Boginski says. "We have shown that this approach can indeed find previously unknown molecules based on their positions in the molecular community network." With the aid of the MCN, Boginski and his team of co-authors have already discovered a new class of bile acids.
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