Fecal Indole as a Biomarker of Susceptibility to Cryptosporidium Infection.
Cynthia L ChappellCharles DarkohLawrence ShimminNaveed FarhanaDo-Kyun KimPablo C OkhuysenJames HixsonPublished in: Infection and immunity (2016)
Cryptosporidium causes significant diarrhea worldwide, especially among children and immunocompromised individuals, and no effective drug treatment is currently available for those who need it most. In this report, previous volunteer infectivity studies have been extended to examine the association between fecal indole and indole-producing (IP) gut microbiota on the outcome of a Cryptosporidium infection. Fecal indole concentrations (FICs) of 50 subjects and 19 taxa of common gut microbiota, including six IP taxa (11 subjects) were determined in stool samples collected before and after a challenge with Cryptosporidium oocysts. At the baseline, the mean FIC (± the standard deviation) was 1.66 ± 0.80 mM in those who became infected after a challenge versus 3.20 ± 1.23 mM in those who remained uninfected (P = 0.0001). Only 11.1% of the subjects with a FIC of >2.5 mM became infected after a challenge versus 65.2% of the subjects with a FIC of <2.5 mM. In contrast, the FICs of infected subjects at the baseline or during diarrhea were not correlated with infection intensity or disease severity. The relative abundances (percent) of Escherichia coli, Bacillus spp., and Clostridium spp. were greater ≥2.5-fold in volunteers with a baseline FIC of >2.5 mM, while those of Bacteroides pyogenes, B. fragilis, and Akkermansia muciniphila were greater in those with a baseline FIC of <2.5 mM. These data indicate that some IP bacteria, or perhaps indole alone, can influence the ability of Cryptosporidium to establish an infection. Thus, preexisting indole levels in the gut join the oocyst dose and immune status as important factors that determine the outcome of Cryptosporidium exposure.
Keyphrases
- escherichia coli
- magnetic resonance
- intensive care unit
- computed tomography
- magnetic resonance imaging
- high intensity
- electronic health record
- cystic fibrosis
- multidrug resistant
- artificial intelligence
- machine learning
- clostridium difficile
- big data
- combination therapy
- extracorporeal membrane oxygenation
- contrast enhanced
- klebsiella pneumoniae
- drug induced
- bacillus subtilis
- antiretroviral therapy
- adverse drug