AN RNA-BASED CLINICAL DIAGNOSTIC ASSAY USED TO IDENTIFY ACTIVE MICROBIAL INFECTIONS
Studies reveal the clinical efficacy of the CSI-Dx assay.

APPLIED RESEARCH

Studies reveal the efficacy of the CSI-Dx technology in a variety of clinical applications.

This study sought to characterize the bacterial and fungal microbiota changes associated with Clostridium difficile infection (CDI) among inpatients with diarrhea, in order to further explain the pathogenesis of this infection as well as to potentially guide new CDI therapies. Twenty-four inpatients with diarrhea were enrolled, 12 of whom had CDI. Each patient underwent stool testing for CDI prior to being treated with difficile-directed antibiotics, when appropriate.

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Clostridium difficile infection (CDI) is the most common nosocomial infection in the United States, being associated with high recurrence and persistence rates. Though the relationship between intestinal dysbiosis and CDI is well known, it is unclear whether different forms of dysbiosis may potentially affect the course of CDI. How this is further influenced by C. difficile-directed antibiotics is virtually uninvestigated.

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Novel culture independent technologies have further elucidated the composition of the human mycobiome, though the role of fungi in human health and disease remains largely unknown. Recent studies have suggested conflicting roles for fungi in the gastrointestinal tract, underscoring the complexity of the interactions between the mycobiome, its bacterial counterpart, and the host. One key example is the observation that fungal taxa are overrepresented in patients with Clostridioides difficile infection (CDI), suggesting a role for fungi in this disease.

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While metagenomic (microbial DNA) sequencing technologies can detect the presence of microbes in a clinical sample, it is unknown whether this signal represents dead or live organisms within a microbial community, and also if it maps expressed genes to a functional pathway of interest (e.g. antibiotic resistance.) After RNA metratranscriptomic analysis of synovial fluid and peripheral blood, differential metatranscrptomic signatures for infected vs. noninfected cohorts enabled us to train machine learning algorithms to 84.9% predictive accuracy for infection. A variety of antibiotic resistance genes were also expressed, with high concordance to conventional antibiotic sensitivity data.

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