Systems Biology Approach To Functionally Assess the Clostridioides difficile Pangenome Reveals Genetic Diversity With Discriminatory Power

Authors

Charles J Norsigian
Heather A Danhof
Colleen K Brand
Firas S Midani
Jared T Broddrick
Tor C Savidge
Robert A Britton
Bernhard O Palsson
Jennifer K Spinler
Jonathan M Monk

Publication Date

5-3-2022

Journal

Proceedings of the National Academy of Sciences of the United States of America

DOI

10.1073/pnas.2119396119

PMID

35476524

PMCID

PMC9170149

PubMedCentral® Posted Date

4-27-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Clostridioides, Clostridioides difficile, Cross Infection, Genetic Variation, Humans, Systems Biology, systems biology, pangenomics, Clostridioides difficile, strain typing, metabolic profile

Abstract

Combatting Clostridioides difficile infections, a dominant cause of hospital-associated infections with incidence and resulting deaths increasing worldwide, is complicated by the frequent emergence of new virulent strains. Here, we employ whole-genome sequencing, high-throughput phenotypic screenings, and genome-scale models of metabolism to evaluate the genetic diversity of 451 strains of C. difficile. Constructing the C. difficile pangenome based on this set revealed 9,924 distinct gene clusters, of which 2,899 (29%) are defined as core, 2,968 (30%) are defined as unique, and the remaining 4,057 (41%) are defined as accessory. We develop a strain typing method, sequence typing by accessory genome (STAG), that identifies 176 genetically distinct groups of strains and allows for explicit interrogation of accessory gene content. Thirty-five strains representative of the overall set were experimentally profiled on 95 different nutrient sources, revealing 26 distinct growth profiles and unique nutrient preferences; 451 strain-specific genome scale models of metabolism were constructed, allowing us to computationally probe phenotypic diversity in 28,864 unique conditions. The models create a mechanistic link between the observed phenotypes and strain-specific genetic differences and exhibit an ability to correctly predict growth in 76% of measured cases. The typing and model predictions are used to identify and contextualize discriminating genetic features and phenotypes that may contribute to the emergence of new problematic strains.

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