An automated system for interrogating the evolution of microbial endosymbiosis.

Inter-kingdom endosymbiotic interactions between bacteria and eukaryotic cells are critical to human health and disease. However, the molecular mechanisms that drive the emergence of endosymbiosis remain obscure. Here, we describe the development of a microfluidic system, named SEER (S̲ystem for the E̲volution of E̲ndosymbiotic R̲elationships), that automates the evolutionary selection of bacteria with enhanced intracellular survival and persistence within host cells, hallmarks of endosymbiosis. Using this system, we show that a laboratory strain of Escherichia coli that initially possessed limited abilities to survive within host cells, when subjected to SEER selection, rapidly evolved to display a 55-fold enhancement in intracellular survival. Notably, molecular dissection of the evolved strains revealed that a single-point mutation in a flexible loop of CpxR, a gene regulator that controls bacterial stress responses, substantially contributed to this intracellular survival. Taken together, these results establish SEER as the first microfluidic system for investigating the evolution of endosymbiosis, show the importance of CpxR in endosymbiosis, and set the stage for evolving bespoke inter-kingdom endosymbiotic systems with novel or emergent properties.

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR.

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Independent Quantification of Electron and Ion Diffusion in Metallocene-Doped Metal-Organic Frameworks Thin Films.

The chronoamperometric response (I vs t) of three metallocene-doped metal-organic frameworks (MOFs) thin films (M-NU-1000, M = Fe, Ru, Os) in two different electrolytes (tetrabutylammonium hexafluorophosphate [TBAPF6] and tetrabutylammonium tetrakis(pentafluorophenyl)borate [TBATFAB]) was utilized to elucidate the diffusion coefficients of electrons and ions (De and Di, respectively) through the structure in response to an oxidizing applied bias. The application of a theoretical model for solid state voltammetry to the experimental data revealed that the diffusion of ions is the rate-determining step at the three different time stages of the electrochemical transformation: an initial stage characterized by rapid electron diffusion along the crystal-solution boundary (stage A), a second stage that represents the diffusion of electrons and ions into the bulk of the MOF crystallite (stage B), and a final period of the conversion dominated only by the diffusion of ions (stage C). Remarkably, electron diffusion (De) increased in the order of Fe < Ru < Os using PF61- as the counteranion in all the stages of the voltammogram, demonstrating the strategy to modulate the rate of electron transport through the incorporation of rapidly self-exchanging molecular moieties into the MOF structure. The De values obtained with larger TFAB1- counteranion were generally in agreement with the previous trend but were on average lower than those obtained with PF61-. Similarly, the ion diffusion coefficient (Di) was generally higher for TFAB1- than for PF61- as the ions diffuse into the crystal bulk, due to the high degree of ion-pair association between PF61- and the metallocenium ion, resulting in a faster penetration of the weakly associated TFAB1- anion through the MOF pores. These structure-function relationships provide a foundation for the future design, control, and optimization of electron and ion transport properties in MOF thin films.