Over the past several years, a wealth of scientific information has become available courtesy of the efforts from scientists studying the microbiome. However, we have always come across one major issue, which is how to harness the plethora of information the research provides in an efficient manner. Now, an international team of investigators, led by researchers at the Chinese Academy of Sciences (CAS) in Beijing, has put forth a new microbiome search-based method via Microbiome Search Engine (MSE) to analyze the wealth of available health data to detect and diagnose human diseases. Findings from the new study were published recently in mSystems through an article entitled “Multiple-Disease Detection and Classification across Cohorts via Microbiome Search.

“Microbiome-based disease classification depends on well-validated, disease-specific models or markers,” explained lead study investigator Xiaoquan Su, PhD, a research scientist at the Single-Cell Center within the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of CAS. “However, current models lack that information for many diseases.”

Multiple diseases can share the same biomarkers—the microorganisms that indicate something out of the ordinary, such as a mutated protein found in cancer cells, making it harder for researchers to classify each one correctly.

To combat these issues for disease detection and classification, the researchers developed a new search approach based on the whole microbial community a human body contains—the microbiome.

“We present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compare these to databases of samples from patients,” the authors wrote. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an essential first step in microbiome big-data-based diagnosis.”

Traditional models compare samples from healthy subjects to those from people known to have specific diseases. With the new method, by searching based on the particular outlier, rather than known biomarkers that can code for several diseases, the researchers can identify the microbiome state associated with the disease across different cohorts or sequencing platforms.

In this new approach, the research team employs a two-step process to identify disease. First, they search a baseline database of healthy individuals to detect any specific microbiome outlier novelty—or any known anomaly that differentiates the microbiome from a healthy state. They then search for that outlier in a database of disease-specific examples.

“Our strategy’s precision, sensitivity, and speed outperform model-based approaches,” SU said.

The results of the search can provide quick predictions to help clinicians diagnose and treat diseases.

“This search-based strategy shows promise as an important first step in microbiome big data-based diagnosis,” according to Rob Knight, PhD, Director of the Center for Microbiome Innovation at UCSD and who recently addressed the GEN audience in a Keynote Webinar on the Dynamic Microbiome. “In light of the general shift of microbiome-sequencing focus from healthy to diseased hosts, the findings here advocate for adding more baseline samples from across different geographic locations.”

The team is working towards encouraging their colleagues to join a coordinated effort to continue expanding the microbiome database, to include every population and every ecosystem on the globe.

“With Microbiome Search Engine, performing a search can become as standard and enabling for new microbiome studies as performing a BLAST against your new DNA sequence is today” concluded XU Jian, Director of Single-Cell Center, QIBEBT.

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