January 2026

Title: The future of microbiology

Journal: Nature Microbiology 

Comment: Jacob Smith interviews microbiologists about what’s next for the field, and a consistent theme is optimism: expanding beyond model organisms to explore the understudied microbial majority (a 2025 preprint suggests ~75% of known bacterial species don’t have a single paper devoted to them!), leveraging machine learning to accelerate discovery (notably for antibiotics), advancing more targeted vaccines and phage therapies, and further breaking down disciplinary silos. 

I also wasn’t aware of the study by Maier and colleagues showing that some human gut microbes (especially Bacteroides uniformis) can bioaccumulate PFAS “forever chemicals” inside their cells while continuing to grow. They also reported that mice colonised with high-bioaccumulating bacteria excreted higher levels of PFNA in faeces. This makes me wonder whether we could focus part of our analysis on these high PFAS-bioaccumulating taxa and test whether their abundance (and/or related functional signatures) is associated with relevant health outcomes...

Title: Multi-omic definition of metabolic obesity through adipose tissue–microbiome interactions

Journal: Nature Medicine 

Comment: Obesity’s metabolic heterogeneity is not fully captured by body mass index. The authors propose a practical way to redefine “obesity” around metabolic injury, rather than BMI, by developing a metabolomics-derived obesity score that is tightly linked to adipose tissue dysfunction and to gut microbiome ecology and function. Using this metabolome-informed obesity metric (metBMI), the two metBMI-derived groups (high and low) differ not only in microbiome gene richness, but also in which organisms are present and how those organisms are organized as an ecological system. Low metBMI shows a more stable, interconnected consortium, in which certain taxa (particularly a Christensenellales member and Methanobrevibacter smithii) sit at the core of many inferred interactions, whereas high metBMI networks are sparser and centered around taxa linked to metabolic dysfunction (for example, Blautia, Bacteroides, Flavonifractor, Erysipeloclostridium ramosum and Ruminococcus gnavus). We have been seeing similar connections in our studies of the human archaeome in relation to metabolism!!!