Correct folding of outer membrane proteins (OMPs) by the β-barrel assembly machinery (BAM) is essential for maintaining the outer membrane (OM) barrier function of diderm bacteria. When OMP biogenesis is perturbed, the β-barrel assembly enhancing protease A (BepA) binds to BAM to mediate quality control, but how BepA interacts with BAM and degrades substrate OMPs remains unclear. Here, cryoEM structures of BAM-bound BepA reveals that BepA induces large conformational changes in the BAM complex enabling the enzyme to poise its active site within the periplasmic ring of BAM, beneath the BamA barrel. The lid of BepA is dynamic, embedding two of its water-soluble helices deep into the membrane bilayer when BAM-bound, which readies BepA for proteolysis of misfolding OMPs. Movement of BepA’s plug is triggered by OMP binding rather than interaction with BAM, activating the enzyme for cleavage. We reveal BepA preferentially recognises Aromatic-X-Aromatic (Ar-X-Ar) motifs which are enriched in OMP sequences. The results reveal a mechanism for proteolytic degradation by BepA in OMP quality control which requires interaction with BAM, the membrane, and its OMP substrates. Here the authors show that BepA is activated to degrade stalled outer membrane proteins on BAM through binding to BAM, inducing conformational changes, membrane embedding its water-soluble lid, and triggering plug movement upon substrate binding to enable proteolysis.

Metrics such as alpha diversity, inferred functional potential and network complexity have become standard metrics in microbiome research. While they offer convenient ways to summarize complex data, these metrics may sometimes obscure more than they reveal. Alpha diversity, for example, measures richness and evenness. However, two samples may exhibit identical diversity scores, yet one could be dominated by beneficial taxa and the other by pathogens. Similarly, the presence of genes associated with particular functions does not guarantee that those functions are expressed or ecologically relevant under given conditions. Functional inference is also limited by database bias and often lacks empirical validation. Likewise, correlation-based network analyses can produce spurious associations driven by shared environmental covariates, sequencing depth or batch effects. These issues are routinely encountered in genomic workflows – from 16S/ITS amplicon surveys to shotgun metagenomics, genome-resolved metagenomics and gene-centric network analyses – where apparently ‘clean’ summary metrics can mask very different ecological realities. Here, we use simple, domain-relevant examples to illustrate how over-reliance on these metrics can lead to misinterpretation. Rather than rejecting these approaches, we outline when they are most informative, when they require caution and what complementary analyses can strengthen ecological inference. We propose a practical framework based on four questions: what exactly is being summarized, at what biological level, under which ecological conditions and with what form of validation? While acknowledging their value, we argue for greater critical scrutiny in their application and interpretation, and advocate for approaches that prioritize functional validation, temporal resolution and systems thinking to support more meaningful ecological insight.