Ruggedness testing of liquid chromatography-tandem mass spectrometry system components using microbiome-relevant methods and matrices

Clostridioides difficile colonization is not mediated by bile salts and utilizes Stickland fermentation of proline in an in vitro model

Treatment with antibiotics is a major risk factor for Clostridioides difficile infection, likely due to depletion of the gastrointestinal microbiota. Two microbiota-mediated mechanisms thought to limit C. difficile colonization include the conversion of conjugated primary bile salts into secondary bile salts toxic to C. difficile growth and competition between the microbiota and C. difficile for limiting nutrients. Using a continuous flow model that simulates the nutrient conditions of the distal colon, we investigated how treatment with 6 clinically used antibiotics influenced susceptibility to C. difficile infection in 12 different microbial communities cultivated from healthy individuals. Antibiotic treatment reduced microbial richness; disruption varied by antibiotic class and microbiota composition, but did not correlate with C. difficile susceptibility. Antibiotic treatment also disrupted microbial bile salt metabolism, increasing levels of the primary bile salt, cholate. However, changes in bile salt did not correlate with increased C. difficile susceptibility. Furthermore, bile salts were not required to inhibit C. difficile colonization. We tested whether amino acid fermentation contributed to the persistence of C. difficile in antibiotic-treated communities. C. difficile mutants unable to use proline as an electron acceptor in Stickland fermentation due to disruption of proline reductase (prdB-) had significantly lower levels of colonization than wild-type strains in four of six antibiotic-treated communities tested. The inability to ferment glycine or leucine as electron acceptors, however, was not sufficient to limit colonization in any communities. The data provide further support for the importance of bile salt-independent mechanisms in regulating the colonization of C. difficile.IMPORTANCEClostridioides difficile is one of the leading causes of hospital-acquired infections and antibiotic-associated diarrhea. Several potential mechanisms through which the microbiota can limit C. difficile infection have been identified and are potential targets for new therapeutics. However, it is unclear which mechanisms of C. difficile inhibition represent the best targets for the development of new therapeutics. These studies demonstrate that in a complex in vitro model of C. difficile infection, colonization resistance is independent of microbial bile salt metabolism. Instead, the ability of C. difficile to colonize is dependent upon its ability to metabolize proline, although proline-dependent colonization is context dependent and is not observed in all disrupted communities. Altogether, these studies support the need for further work to understand how bile-independent mechanisms regulate C. difficile colonization.

Phase I trial comparing bile acid and short-chain fatty acid alterations in stool collected from human subjects treated with omadacycline or vancomycin

Omadacycline, an aminomethylcycline tetracycline, has a low propensity to cause Clostridioides difficile infection (CDI) in clinical trials. Omadacycline exhibited a reduced bactericidal effect compared with vancomycin on key microorganisms implicated in bile acid homeostasis and short-chain fatty acids (SCFAs), key components of CDI pathogenesis. The purpose of this study was to assess bile acid and SCFA changes in stool samples from healthy volunteers given omadacycline or vancomycin. Stool samples were collected daily from 16 healthy volunteers, who were given oral omadacycline or vancomycin for 10 days. Daily stool samples were assessed for bile acids and SCFA concentrations using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Bile acids changed significantly over time for all subjects (P < 0.01 for each bile acid), with vancomycin causing a larger change in the primary bile acids, cholic acid (P < 0.001) and chenodeoxycholic acid (P < 0.001), and a reduced change in the secondary bile acid, lithocholic acid (P < 0.001). The secondary bile acid ursodeoxycholic acid was reduced less by vancomycin than by omadacycline (P < 0.001). All SCFA concentrations were reduced from baseline with a larger effect observed with vancomycin for isobutyric acid (P = 0.0034), propionic acid (P = 0.0012), and acetic acid (P = 0.047). Microbial changes associated with the use of vancomycin versus omadacycline were also associated with changes in bile acid homeostasis and SCFA concentrations. Oral omadacycline produced a distinctive metabolomic profile compared with vancomycin when administered to healthy subjects. The metabolic findings help further our understanding of the lower CDI risk properties of omadacycline and warrant phase 2 investigations using omadacycline as a CDI antibiotic.

IMPORTANCE

The purpose of this study was to assess bile acid and SCFA changes in stool samples obtained from healthy volunteers given omadacycline or vancomycin. Stool samples were collected daily from 16 healthy volunteers given a 10-day oral course of omadacycline or vancomycin. Vancomycin caused a larger change in the primary bile acids and SCFA concentrations compared with omadacycline. The metabolic findings help further our understanding of the mechanistic basis for the lower-risk properties of omadacycline causing CDI and warrant phase 2 investigations using omadacycline as a CDI antibiotic.

CLINICAL TRIALS

This study is registered with ClinicalTrials.gov as NCT06030219.

Clostridioides difficile colonization is not mediated by bile salts and utilizes Stickland fermentation of proline in an in vitro model

Treatment with antibiotics is a major risk factor for Clostridioides difficile infection, likely due to depletion of the gastrointestinal microbiota. Two microbiota-mediated mechanisms thought to limit C. difficile colonization include conversion of conjugated primary bile salts into secondary bile salts toxic to C. difficile growth, and competition between the microbiota and C. difficile for limiting nutrients. Using a continuous flow model that simulates the nutrient conditions of the distal colon, we investigated how treatment with six clinically-used antibiotics influenced susceptibility to C. difficile infection in 12 different microbial communities cultivated from healthy individuals. Antibiotic treatment reduced microbial richness; disruption varied by antibiotic class and microbiota composition, but did not correlate with C. difficile susceptibility. Antibiotic treatment also disrupted microbial bile salt metabolism, increasing levels of the primary bile salt, cholate. However, changes in bile salt did not correlate with increased C. difficile susceptibility. Further, bile salts were not required to inhibit C. difficile colonization. We tested whether amino acid fermentation contributed to persistence of C. difficile in antibiotic-treated communities. C. difficile mutants unable to use proline as an electron acceptor in Stickland fermentation due to disruption of proline reductase (prdB-) had significantly lower levels of colonization than wild-type strains in four of six antibiotic-treated communities tested. Inability to ferment glycine or leucine as electron acceptors, however, was not sufficient to limit colonization in any communities. This data provides further support for the importance of bile salt-independent mechanisms in regulating colonization of C. difficile.

Mathematical models to study the biology of pathogens and the infectious diseases they cause

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause.