on the limits of 16s rrna gene-based functional profiling

posted on may 14, 2024   by monica matchado and markus list

monica matchado and markus list take us behind the scenes of their latest publication 'on the limits of 16s rrna gene-based metagenome prediction and functional profiling' published in microbial genomics. 

the gut microbiome is inherently linked to human health through various physiological processes ranging from digestion to more complex activities such as modulating immune responses. most of the research thus far was aimed at studying microbial richness and composition. characterizing individual taxa within the microbiome is often achieved through 16s rrna gene sequencing, a robust and inexpensive tool offering a snapshot of microbial diversity within different environments, including the human body. this approach has greatly advanced our understanding of the composition and structure of microbial communities, leading to significant discoveries in the field of microbiome research. 

towards studying microbial functions

while a census of microbial communities remains an essential aspect, research has moved more toward studying the functional potential of the microbiome and its influence on host physiology. microbial functions encompass a wide array of activities, including metabolism, signaling, and interactions with the host immune system. these functions dictate the role of the microbiome in various physiological processes and disease states. to explore these functions, studying the genes and their expression within microbial communities becomes crucial.

challenges with 16s rrna gene-based functional profiling

metagenomics, which provides a comprehensive view of microbial genetic content, is more suitable for functional characterization than 16s rrna gene sequencing. since metagenomics is more expensive, it can often not be afforded for large-scale population or disease cohorts, even though we should strive to study the microbiome across as many samples as possible given its vast variability across geography, lifestyle and even individuals. to characterize large cohorts, researchers thus often continue to rely on 16s rrna gene sequencing. an important question for the field has thus been whether we can still gain insights into the functional potential of the microbiome from the limited information provided by 16s rrna data. to close this gap, several computational methods have been developed to infer functional profiles from a census of microbial abundances. to achieve this, such methods leverage known genomes of the identified taxa and infer their putative functional potential from the genes they encode. the quality of 16s-based functional inference with respect to studying human health has thus far remained understudied.

comparing 16s rrna inferred against metagenomics as a ground truth

this motivated us to conduct a benchmark study of computational methods for predicting or inferring functional profiles from 16s rrna gene sequencing data. notably, we compare functional profiling results against matched metagenomics data as a ground truth. while metagenomics is likely biased itself, this comparison still allows us to assess the efficacy of 16s rrna gene-based functional profiling. in contrast to previous studies that assessed the overall performance of functional inference, we focused on the question of whether these methods are sensitive enough to robustly pick up differences in functional activities that are related to human health. 

limitations of 16s rrna-based functional profiling

our benchmark study revealed discrepancies between functional profiles inferred from 16s rrna gene data and from metagenomics. while 16s rrna gene sequencing accurately captured microbial diversity, it often underestimated or misinterpreted functional capabilities. our analysis also highlighted important differences among the individual tools tested. while some tools performed better than others in certain aspects, none exhibited the level of sensitivity required for robust functional profiling. based on our findings, we offer recommendations for tool selection, emphasizing the importance of considering factors such as sample characteristics and research objectives.

16s rrna-based functional profiling should be handled with care when studying human health

in conclusion, our benchmark studies highlight the importance of critically evaluating the efficacy of 16s rrna gene-based functional profiling in microbiome research. while 16s rrna gene sequencing is commonly used in large population cohort studies, its capacity for microbial functional characterization is limited. all functional prediction tools we tested showed poor sensitivity in detecting differences related to human health and disease. moving forward, researchers should carefully consider the limitations of 16s rrna gene analysis and consider alternatives such as (shallow) metagenomics, metatranscriptomics, meta-metabolomics, or metaproteomics, for a more comprehensive understanding of microbiome function and its implications for human health.

through this scientific blog, we aim to raise awareness about the challenges and limitations of 16s rrna gene-based functional profiling and encourage the adoption of more robust and accurate techniques in microbiome research