Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions

Fitness benefits from division of labor are well documented in microbial consortia, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a “push” interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a “pull” interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild-type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of by-product accumulation all relative to the WT. However, the LAE consortium had the trade-off of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered performance metric. Performance advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the performance advantages compared to WT

Biophysical strategies to inhibit bacterial amyloid formation and undermine biofilm infections

Thesis (Ph.D.)--University of Washington, 2019When bacteria dwell in biofilms on the surface of an implanted medical device or surgical site, cells co-associate using a self-produced extracellular matrix (EM), which acts as a layer of protection against antibiotic infiltration. The EM is comprised of polysaccharides, DNA, and proteins including amyloid fibrils. Extracellular deposition of amyloid has long been associated with protein misfolding and neurodegenerative diseases, but a growing body of research demonstrates that bacteria have adopted amyloid fibrils as a functional scaffold to reinforce the biofilm. Consequently, these functional amyloids represent a novel target to interrupt biofilm formation and address the problems of biofilm-associated infection and resistance. The pathway to amyloid formation in the EM is characterized by specific physicochemical motifs; therefore, peptides engineered to bind these motifs should inhibit fibril formation and disrupt biofilm development. Here, the mechanism of amyloid formation is analyzed in both Gram positive and Gram negative bacteria, peptide design approaches are introduced to inhibit amyloid formation, and the results are translated to clinically relevant models

Designed α-sheet peptides disrupt uropathogenic E. coli biofilms rendering bacteria susceptible to antibiotics and immune cells

Abstract Uropathogenic Escherichia coli account for the largest proportion of nosocomial infections in the United States. Nosocomial infections are a major source of increased costs and treatment complications. Many infections are biofilm associated, rendering antibiotic treatments ineffective or cause additional complications (e.g., microbiome depletion). This work presents a potentially complementary non-antibiotic strategy to fight nosocomial infections by inhibiting the formation of amyloid fibrils, a proteinaceous structural reinforcement known as curli in E. coli biofilms. Despite extensive characterization of the fibrils themselves and their associated secretion system, mechanistic details of curli assembly in vivo remain unclear. We hypothesized that, like other amyloid fibrils, curli polymerization involves a unique secondary structure termed “α-sheet”. Biophysical studies herein confirmed the presence of α-sheet structure in prefibrillar species of CsgA, the major component of curli, as it aggregated. Binding of synthetic α-sheet peptides to the soluble α-sheet prefibrillar species inhibited CsgA aggregation in vitro and suppressed amyloid fibril formation in biofilms. Application of synthetic α-sheet peptides also enhanced antibiotic susceptibility and dispersed biofilm-resident bacteria for improved uptake by phagocytic cells. The ability of synthetic α-sheet peptides to reduce biofilm formation, improve antibiotic susceptibility, and enhance clearance by macrophages has broad implications for combating biofilm-associated infections

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440.

UNLABELLED: There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (functional modules), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE: This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets

Chemical and Physical Variability in Structural Isomers of an l/d α‑Sheet Peptide Designed To Inhibit Amyloidogenesis

There has been much interest in synthetic peptides as inhibitors of aggregation associated with amyloid diseases. Of particular interest are compounds that target the cytotoxic soluble oligomers preceding the formation of mature, nontoxic fibrils. This study explores physical and chemical differences between two <i>de novo</i>-designed peptides that share an identical primary structure but differ in backbone chirality at six key positions. We show that the presence of alternating l/d-amino acid motifs dramatically increases aqueous solubility, enforces α-sheet secondary structure, and inhibits aggregation of the β-amyloid peptide implicated in Alzheimer’s disease, in addition to neutralizing its cytotoxicity. In contrast, the all-l-amino acid isomer does not form α-sheet structure and is insoluble and inactive

Multiplexed fitness profiling by RB-TnSeq elucidates pathways for lignin-related aromatic catabolism in Sphingobium sp. SYK-6

Summary: Bioconversion of lignin-related aromatic compounds relies on robust catabolic pathways in microbes. Sphingobium sp. SYK-6 (SYK-6) is a well-characterized aromatic catabolic organism that has served as a model for microbial lignin conversion, and its utility as a biocatalyst could potentially be further improved by genome-wide metabolic analyses. To this end, we generate a randomly barcoded transposon insertion mutant (RB-TnSeq) library to study gene function in SYK-6. The library is enriched under dozens of enrichment conditions to quantify gene fitness. Several known aromatic catabolic pathways are confirmed, and RB-TnSeq affords additional detail on the genome-wide effects of each enrichment condition. Selected genes are further examined in SYK-6 or Pseudomonas putida KT2440, leading to the identification of new gene functions. The findings from this study further elucidate the metabolism of SYK-6, while also providing targets for future metabolic engineering in this organism or other hosts for the biological valorization of lignin

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.</p

Catalytic carbon–carbon bond cleavage in lignin via manganese–zirconium-mediated autoxidation

Abstract Efforts to produce aromatic monomers through catalytic lignin depolymerization have historically focused on aryl–ether bond cleavage. A large fraction of aromatic monomers in lignin, however, are linked by various carbon–carbon (C–C) bonds that are more challenging to cleave and limit the yields of aromatic monomers from lignin depolymerization. Here, we report a catalytic autoxidation method to cleave C–C bonds in lignin-derived dimers and oligomers from pine and poplar. The method uses manganese and zirconium salts as catalysts in acetic acid and produces aromatic carboxylic acids as primary products. The mixtures of the oxygenated monomers are efficiently converted to cis,cis-muconic acid in an engineered strain of Pseudomonas putida KT2440 that conducts aromatic O-demethylation reactions at the 4-position. This work demonstrates that autoxidation of lignin with Mn and Zr offers a catalytic strategy to increase the yield of valuable aromatic monomers from lignin