September 14, 2022
Sugar Substitutes, aka Non-Nutritive Sweeteners
It has long been assumed that replacing some of the sugar in your diet with calorie-free artificial sweeteners will satisfy your sweet tooth without affecting weight gain or metabolism. However, over the years contradicting studies have pointed to sugar substitutes potentially having the opposite effect and antagonizing weight gain and glucose tolerance.
In a recent Cell paper, Suez et. al. investigated the effects of different sugar substitutes (or non-nutritive sweeteners) on the microbiome using a randomized-controlled trial and subsequent bioinformatics analysis.
Effects on the Microbiome
The authors’ most striking result was that each of the four sweeteners tested produced a unique effect on the gut microbiome. Some of these effects were neutral, as in there was a measurable change to the composition of the microbiome but no associated effects on glucose levels in the blood (glycemia). However, some participants developed impaired glucose metabolism as a result of sweetener consumption, particularly the groups consuming saccharin (commonly marketed as Sweet-N-Low) and sucralose (Splenda).
To establish a causal relationship between the impared glucose tolerance and the changes to the microbiome, the authors performed fecal transplants from participants with a negative glycemic response to the sweetener into mice with normal glucose tolerance, no exposure to artificial sweeteners and a “neutral” microbiome. The microbial transplant alone produced the same result in these mice, confirming that the alterations made to the microbiome by the artificial sweeteners were sufficient to affect glucose metabolism.
Outsourcing Bioinformatics Analysis
This study leveraged metagenomics, specifically shotgun metagenomic sequencing, for their microbiome analysis. The more common method for profiling microbial communities uses 16S rRNA gene sequencing which only allows for reliable bacterial classification to the genus level, cannot reliably identify archaea and fungi, and provides little insight into metabolic processes.
With shotgun metagenomics however, bacteria can be classified to the species or strain level, DNA from archaea and fungi are captured and can be identified, and metabolic profiles of microbes within a community can be inferred. For analysis of big genomic data, bioinformatics-as-a-service (BaaS) providers like Bridge Informatics can help you with data storage and creating robust, reproducible analytical pipelines. Book a free discovery call to see how we can help with your project needs.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email daniel.dacey@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.
Sources:
https://doi.org/10.1016/j.cell.2022.08.007
https://doi.org/10.1016/j.cell.2022.07.016
