Biology subjects

Waldor, M. K.

Publications and source records attributed to Waldor, M. K..

4 recordsLinked to original sources

bioRxiv2018

Genetic determinants of penicillin tolerance in Vibrio cholerae

Many bacteria are resistant to killing (\"tolerant\") by typically bactericidal antibiotics due to their ability to counteract drug-induced cell damage. Vibrio cholerae, the cholera agent, displays an unusually high tolerance to diverse inhibitors of cell wall synthesis. Exposure to these agents, which in other bacteria leads to lysis and death, results in a breakdown of the cell wall and subsequent sphere formation in V. cholerae. Spheres readily recover to rod-shaped cells upon antibiotic removal, but the mechanisms mediating the recovery process are not well-characterized. Here, we found that the mechanisms of recovery are dependent on environmental conditions. Interestingly, on agarose pads, spheres undergo characteristic stages during the restoration of rod shape. Drug inhibition and microscopy experiments suggest that class A Penicillin Binding Proteins (aPBPs) play a more active role than the Rod system, especially early in sphere recovery. TnSeq analyses revealed that LPS and cell wall biogenesis genes as well as the sigma E cell envelope stress response were particularly critical for recovery. LPS core and O-antigen appear to be more critical for sphere formation/integrity and viability than Lipid A modifications. Overall, our findings demonstrate that the outer membrane is a key contributor to beta lactam tolerance and suggest a role for aPBPs in cell wall biogenesis in the absence of rod-shape cues. Factors required for post-antibiotic recovery could serve as targets for antibiotic adjuvants that enhance the efficacy of antibiotics that inhibit cell wall biogenesis.

microbiology↗

bioRxiv2018

CRISPR screen reveals that EHEC’s T3SS and Shiga toxin rely on shared host factors for infection

Enterohemorrhagic Escherichia coli (EHEC) has two critical virulence factors - a type III secretion system (T3SS) and Shiga toxins (Stx) - that are required for the pathogen to colonize the intestine and cause diarrheal disease. Here, we carried out a genome-wide CRISPR/Cas9 loss-of-function screen to identify host loci that facilitate EHEC infection of intestinal epithelial cells. Many of the guide RNAs identified targeted loci known to be associated with sphingolipid biosynthesis, particularly for production of globotriaosylceramide (Gb3), the Stx receptor. Two loci (TM9SF2 and LAPTM4A) with largely unknown functions were also targeted. Mutations in these loci not only rescued cells from Stx-mediated cell death, but also prevented cytotoxicity associated with the EHEC T3SS. These mutations interfered with early events associated with T3SS and Stx pathogenicity, markedly reducing entry of T3SS effectors into host cells and binding of Stx. The convergence of Stx and T3SS onto overlapping host targets provides guidance for design of new host-directed therapeutic agents to counter EHEC infection.\n\nImportanceEnterohemorrhagic Escherichia coli (EHEC) has two critical virulence factors - a type III secretion system (T3SS) and Shiga toxins (Stx) - that are required for colonizing the intestine and causing diarrheal disease. We screened a genome-wide collection of CRISPR mutants derived from intestinal epithelial cells and identified mutants with enhanced survival following EHEC infection. Many had mutations that disrupted synthesis of a subset of lipids (sphingolipids) that includes the Stx receptor globotriaosylceramide (Gb3), and hence protect against Stx intoxication. Unexpectedly, we found that sphingolipids also mediate early events associated with T3SS pathogenicity. Since antibiotics are contraindicated for the treatment of EHEC, therapeutics targeting sphingolipid biosynthesis are a promising alternative, as they could provide protection against both of the pathogens key virulence factors.

microbiology↗

bioRxiv2017

Deciphering Functional Redundancy in the Human Microbiome

Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved1-5---implying an ecological property known as functional redundancy. Such functional redundancy is thought to underlie the stability and resilience of the human microbiome6,7, but its origin is elusive. Here, we investigate the basis for functional redundancy in the human microbiome by analyzing its genomic content network --- a bipartite graph that links microbes to the genes in their genomes. We show that this network exhibits several topological features, such as highly nested structure and fat-tailed gene degree distribution, which favor high functional redundancy. To explain the origins of these topological features, we develop a simple genome evolution model that explicitly considers selection pressure, and the processes of gene gain and loss, and horizontal gene transfer. We find that moderate selection pressure and high horizontal gene transfer rate are necessary to generate genomic content networks with both highly nested structure and fat-tailed gene degree distribution, and consequently favor high functional redundancy. These findings provide insights into the relationships between structure and function in complex microbial communities. This work elucidates the potential ecological and evolutionary processes that create and maintain functional redundancy in the human microbiome and contribute to its resilience.

microbiology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗