Biology subjects

Bamert, R. S.

Publications and source records attributed to Bamert, R. S..

3 recordsLinked to original sources

bioRxiv2021

Discovery of a conserved rule behind the assembly of β-barrel membrane proteins.

Outer membrane proteins (OMPs) are essential components of the outer membrane of Gram-negative bacteria. In terms of protein targeting and assembly, the current dogma holds that a "{beta}-signal" imprinted in the final {beta}-strand of the OMP engages the {beta}-barrel assembly machinery (BAM complex) to initiate membrane insertion and assembly of the OMP into the outer membrane. Here, we revealed an additional rule, that signals equivalent to the {beta}-signal are repeated in other, internal {beta}-strands within bacterial OMPs, by peptidomimetic and mutational analysis. The internal signal is needed to promote the efficiency of the assembly reaction of these OMPs. BamD, an essential subunit of the BAM complex, recognizes the internal signal and the {beta}-signal, arranging several {beta}-strands and partial folding for rapid OMP assembly. The internal signal-BamD ordering system is not essential for bacterial viability but is necessary to retain the integrity of the outer membrane against antibiotics and other environmental insults. TEASERBacterial outer membrane proteins are recognized and bound by BamD at specific signals located in multiple {beta}-strands at the C-terminus of these proteins.

biochemistry↗

bioRxiv2021

Bacteriophage adaptation to a mammalian mucosa reveals a trans-domain evolutionary axis

The majority of viruses within the human gut are obligate bacterial viruses known as bacteriophages (phages)1. Their bacteriotropism underscores the study of phage ecology in the gut, where they sustain top-down control2-4 and co-evolve5 with gut bacterial communities. Traditionally, these were investigated empirically via in vitro experimental evolution6-8 and more recently, in vivo models were adopted to account for gut niche effects4,9. Here, we probed beyond conventional phage-bacteria co-evolution to investigate the potential evolutionary interactions between phages and the mammalian "host". To capture the role of the mammalian host, we recapitulated a life-like mammalian gut mucosa using in vitro lab-on-a-chip devices (to wit, the gut-on-a-chip) and showed that the mucosal environment supports stable phage-bacteria co-existence. Next, we experimentally evolved phage populations within the gut-on-a-chip devices and discovered that phages adapt by de novo mutations and genetic recombination. We found that a single mutation in the phage capsid protein Hoc - known to facilitate phage adherence to mucus10 - caused altered phage binding to fucosylated mucin glycans. We demonstrated that the altered glycan-binding phenotype provided the evolved mutant phage a competitive fitness advantage over their ancestral wildtype phage in the gut-on-a-chip mucosal environment. Collectively, our findings revealed that phages - in addition to their evolutionary relationship with bacteria - are also able to engage in evolution with the mammalian host.

microbiology↗

bioRxiv2021

The component parts of bacteriophage virions accurately defined by a machine-learning approach built on evolutionary features.

Antimicrobial resistance (AMR) continues to evolve as a major threat to human health and new strategies are required for the treatment of AMR infections. Bacteriophages (phages) that kill bacterial pathogens are being identified for use in phage therapies, with the intention to apply these bactericidal viruses directly into the infection sites in bespoke phage cocktails. Despite the great unsampled phage diversity for this purpose, an issue hampering the roll out of phage therapy is the poor quality annotation of many of the phage genomes, particularly for those from infrequently sampled environmental sources. We developed a computational tool called STEP3 to use the "evolutionary features" that can be recognized in genome sequences of diverse phages. These features, when integrated into an ensemble framework, achieved a stable and robust prediction performance when benchmarked against other prediction tools using phages from diverse sources. Validation of the prediction accuracy of STEP3 was conducted with high-resolution mass spectrometry analysis of two novel phages, isolated from a watercourse in the Southern Hemisphere. STEP3 provides a robust computational approach to distinguish specific and universal features in phages to improve the quality of phage cocktails, and is available for use at http://step3.erc.monash.edu/. IMPORTANCEIn response to the global problem of antimicrobial resistance there are moves to use bacteriophages (phages) as therapeutic agents. Selecting which phages will be effective therapeutics relies on interpreting features contributing to shelf-life and applicability to diagnosed infections. However, the protein components of the phage virions that dictate these properties vary so much in sequence that best estimates suggest failure to recognize up to 90% of them. We have utilised this diversity in evolutionary features as an advantage, to apply machine learning for prediction accuracy for diverse components in phage virions. We benchmark this new tool showing the accurate recognition and evaluation of phage components parts using genome sequence data of phages from under-sampled environments, where the richest diversity of phage still lies.

microbiology↗