The team of researchers has proposed a new revolutionary microbiome search-based method through Microbiome Search Engine (MSE) to analyze the available data for the detection and diagnosis of the disease.
Dr SU Xiaoquan from the Single-Cell Center at Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences (QIBEBT) said microbiome-based disease detection and classification rely on the well-validated, disease-specific markers or models, however, current markers are lacking the certain information for many diseases.
He added that many diseases may share the same biomarkers, the microorganisms that indicate something such as mutated protein, making it difficult for researchers to classify each one correctly.
To combat such issues of disease diagnosis and classification, Dr SU and his joint software team from Single-Cell Center, QIBEBT, Center for Microbiome Innovation (CMI) and University of California at San Diego collectively developed the latest search approached based on the microbial community which is contained by the human body, collectively known as the microbiome.
Microbiome associated studies have found diagnostic application in several diseases. Recent strategies which are used for disease detection and diagnosis typically depends on the computational models by identifying gene-based markers from specific cohorts with validated samples from healthy controls and patients through machine learning methods. Then these models are applied to a query for deriving a numeric index of disease severity, status and risk. Extending a model to other studies can be challenging since the selection of biomarkers needs careful consideration of the effect of many factors including sequencing technologies and host metadata.
Conventional models compare samples from healthy individuals to the samples of those known to have some specific diseases. With this latest method, by searching based on the specific outlier, the researchers are able to identify the microbiome associated with the disease across different sequencing platforms and cohorts rather than known markers which can code for various diseases.
In this latest approach, researchers employ the two-step process for disease detection. First, searching the baseline database of healthy people for detecting any microbiome outlier novelty or any known anomaly which differs the microbiome from a normal healthy state then searching that outlier in the database of diseases specific examples.
Dr SU shared that their strategy’s precision, speed and sensitivity outperform the model-based methods. The outcomes of the search can provide quick predictions that will help healthcare professionals to properly diagnose and treat diseases.
With Microbiome Search Engine, performing a search may become as standard in future and enabling for new microbiome-related studies just like performing a BLAST against DNA sequences. The findings advocate for increased geographic sequencing of more baseline samples and the coordinated effort with global sampling coverage is guaranteed.
This revolutionary work is supported by the National Natural Science Foundation of China, Chinese Academy of National Sciences, the National Science Foundation, and the National Institutes of Health, the Alfre P. Sloan Foundation and the National Health and Medical Research Council.