Early prediction of preeclampsia using the first trimester vaginal microbiome.

Preeclampsia is a severe obstetrical syndrome which contributes to 10-15% of all maternal deaths. Although the mechanisms underlying systemic damage in preeclampsia-such as impaired placentation, endothelial dysfunction, and immune dysregulation-are well studied, the initial triggers of the condition remain largely unknown. Furthermore, although the pathogenesis of preeclampsia begins early in pregnancy, there are no early diagnostics for this life-threatening syndrome, which is typically diagnosed much later, after systemic damage has already manifested.

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models.

Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models.

Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission.

Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts.

Unravelling plasmidome distribution and interaction with its hosting microbiome.

Horizontal gene transfer via plasmids plays a pivotal role in microbial evolution. The forces that shape plasmidomes functionality and distribution in natural environments are insufficiently understood. Here, we present a comparative study of plasmidomes across adjacent microbial environments present in different individual rumen microbiomes.

Recycler: an algorithm for detecting plasmids from de novo assembly graphs.

Motivation: Plasmids and other mobile elements are central contributors to microbial evolution and genome innovation. Recently, they have been found to have important roles in antibiotic resistance and in affecting production of metabolites used in industrial and agricultural applications. However, their characterization through deep sequencing remains challenging, in spite of rapid drops in cost and throughput increases for sequencing.

A method for purifying high quality and high yield plasmid DNA for metagenomic and deep sequencing approaches.

Deep sequencing techniques used in metagenomic approaches have greatly advanced the study of microbial communities in various environments. However, one microbial segment that has remained largely unexplored is the natural plasmids residing within microbial environments. Plasmids are perceived as mobile genetic elements that exist extra-chromosomally and occasionally carry accessory genes that confer an advantage to their host in its ecological niche.

Insights into the bovine rumen plasmidome.

Plasmids are self-replicating genetic elements capable of mobilization between different hosts. Plasmids often serve as mediators of lateral gene transfer, a process considered to be a strong and sculpting evolutionary force in microbial environments. Our aim was to characterize the overall plasmid population in the environment of the bovine rumen, which houses a complex and dense microbiota that holds enormous significance for humans.