A biofilm of Geobacter sulfurreducens will grow on an anode surface and catalyze the generation of an electrical current by oxidizing acetate and utilizing the anode as its metabolic terminal electron acceptor. Here we report qualitative analysis of cyclic voltammetry of anodes modified with biofilms of G. sulfurreducens strains DL1 and KN400 to predict possible rate-limiting steps in current generation. Strain KN400 generates approximately 2 to 8-fold greater current than strain DL1 depending upon the electrode material, enabling comparative electrochemical analysis to study the mechanism of current generation. This analysis is based on our recently reported electrochemical model for biofilm-catalyzed current generation expanded here to a five step model; Step 1 is mass transport of acetate, carbon dioxide and protons into and out of the biofilm, Step 2 is microbial turnover of acetate to carbon dioxide and protons, Step 3 is the non-concerted, 1-electron reduction of 8 equivalents of electron transfer (ET) mediator, Step 4 is extracellular electron transfer (EET) through the biofilm to the electrode surface, and Step 5 is the reversible oxidation of reduced mediator by the electrode. Five idealized voltammetric current vs. potential dependencies (voltammograms) are derived, one for when each step in the model is assumed to limit catalytic current. Comparison to experimental voltammetry of DL1 and KN400 biofilm-modified anodes suggests that for both strains, the microbial oxidation of acetate (Step 2) is fast compared to microbial reduction of ET mediator (Step 3), and either Step 3 or EET through the biofilm (Step 4) limits catalytic current generation. The possible limitation of catalytic current by Step 4 is consistent with proton concentration gradients observed within these biofilms and finite thicknesses achieved by these biofilms. The model presented here has been universally designed for application to biofilms other than G. sulfurreducens and could serve as a platform for future quantitative voltammetric analysis of non-corrosive anode and cathode reactions catalyzed by microorganisms.

Summary: Three notable members of the Harveyi clade, Vibrio harveyi , Vibrio alginolyticus and Vibrio parahaemolyticus , are best known as marine pathogens of commercial and medical import. In spite of this fact, the discrimination of Harveyi clade members remains difficult due to genetic and phenotypic similarities, and this has led to misidentifications and inaccurate estimations of a species' involvement in certain environments. To begin to understand the underlying genetics that complicate species level discrimination, we compared the genomes of Harveyi clade members isolated from different environments (seawater, shrimp, corals, oysters, finfish, humans) using microarray‐based comparative genomic hybridization (CGH) and multilocus sequence analyses (MLSA). Surprisingly, we found that the only two V. harveyi strains that have had their genomes sequenced (strains BAA‐1116 and HY01) have themselves been misidentified. Instead of belonging to the species harveyi , they are actually members of the species campbellii . In total, 28% of the strains tested were found to be misidentified and 42% of these appear to comprise a novel species. Taken together, our findings correct a number of species misidentifications while validating the ability of both CGH and MLSA to distinguish closely related members of the Harveyi clade.

New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input parameters. Extensive analysis of the PepVAE-generated sequences paired with antimicrobial activity prediction models supports this modular design framework as a promising system for development of novel AMPs, demonstrating controlled production of AMPs with experimental validation of predicted antimicrobial activity.

The exponential growth of pathogen nucleic acid sequences available in public domain databases has invited their direct use in pathogen detection, identification, and surveillance strategies. DNA microarray technology has offered the potential for the direct DNA sequence analysis of a broad spectrum of pathogens of interest. However, to achieve the practical attainment of this potential, numerous technical issues, especially nucleic acid amplification, probe specificity, and interpretation strategies of sequence detection, need to be addressed. In this report, we demonstrate an approach that combines the use of a custom-designed Affymetrix resequencing Respiratory Pathogen Microarray (RPM v.1) with methods for microbial nucleic acid enrichment, random nucleic acid amplification, and automated sequence similarity searching for broad-spectrum respiratory pathogen surveillance. Successful proof-of-concept experiments, utilizing clinical samples obtained from patients presenting adenovirus or influenza virus-induced febrile respiratory illness (FRI), demonstrate the ability of this approach for correct species- and strain-level identification with unambiguous statistical interpretation at clinically relevant sensitivity levels. Our results underscore the feasibility of using this approach to expedite the early surveillance of diseases, and provide new information on the incidence of multiple pathogens.