Potential Forensic Uses for Human Microbiome
News May 14, 2015
Researchers have tapped into 16S ribosomal RNA and metagenomic sequence datasets generated for individuals enrolled in the Human Microbiome Project to search for microbial signatures that consistently and stably differed between individuals at various body sampling sites.
Within the HMP samples, the team saw microbiome signatures that could successfully differentiate between baseline samples from the individuals. And microbial signatures from the gut microbiome appeared to remain particularly stable and distinct, providing clues for re-identifying roughly 80 percent of the individuals months later.
"Although the potential for any data privacy concerns from purely microbial DNA is very low, it's important for researchers to know that such issues are theoretically possible," senior author Curtis Huttenhower, a biostatistics researcher affiliated with Harvard School of Public Health and the Broad Institute, said in a statement.
"Perhaps even more exciting are the implications of the study for microbial ecology, since it suggests our unique microbial residents are tuned to the environment of our body — our genetics, diet, and developmental history — in such a way that they stick with us and help to fend off less-friendly microbial invaders over time," Huttenhower added.
Although human microbiomes from each body site tend to cluster together, past studies indicate that the microbial composition and gene content at these locations vary significantly from one person to the next. Consequently, it's long been suspected that microbial communities found at certain body sites might have forensic utility.
In a study published in 2010, for example, investigators focused on bacterial community composition in swab samples from computer keyboards belonging to three different individuals to search for skin microbiome signatures with forensic potential.
For the latest study, researchers delved into existing sequence data for several body sites to more fully flesh out the forensic potential of the human microbiome.
Using a so-called hitting set-based algorithm, the team brought together operational taxonomic unit, species, marker gene, and microbial reference sequence data to search for stable and distinct features in samples collected at between six and 18 body sites for as many as 120 HMP participants over weeks or months.
"We applied insights from computing theory and microbial ecology to construct metagenomic codes from sets of individual-specific and maximally stable metagenomic features," the study authors explained.
After tracking down microbiome markers that consistently differed between individuals tested at a given body site at one time point, for example, the investigators checked to see if the same participant-specific signature persisted at later sampling points and attempted to validate their findings using data for additional samples from the HMP and MetaHIT projects.
Indeed, their results suggest that microbial features found at the first time point were stable enough to identify around 30 percent of the individuals at subsequent time points between 30 days and many months later, suggesting it may be possible to use microbial "fingerprints" to forensically distinguish between individuals.
The gut microbiome appeared to show particular promise as an individual identifier, the researchers reported. There, they were able to successfully re-identify some 80 percent of individuals based on microbial signatures present up to a year after sampling individuals' first fecal samples.
"In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability — a result with important ethical implications for microbiome study design," Huttenhower and colleagues concluded.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.