RNA-based infection testing used to identify bacteria, virus, and fungal species
Applying the same technology as CSI-Dx™ in other applications to assist physicians

Applications of RNA-based Infection Testing

Contamination Source Identification technology and physicians have been applying the same technology as CSI-Dx™ in other applications to assist physicians in identifying microbial species in different applications. For more information on this technology’s research and application, please reference any of the studies listed below.

Culture-based methods have been regarded as the gold standard of diagnosis for infectious diseases (Laupland and Valiquette, 2013) and used as an essential tool in determining treatment regimens. However, these methods can take up to 96 hours to identify a pathogen and determine its susceptibility to antibiotics (Afshari et al., 2012). Some organisms, such as the causal agent of Lyme disease, B. burgdorferi, require special media and may take much longer to grow to detectable levels, if they grow at all (Schutzer et al., 2019). Factors that decrease the efficacy of culture-based methods include previous antibiotic treatment, growth media requirements that can be difficult or impossible to replicate, poor sample quality or preprocessing, low microbial load, and minor infection severity (Fenollar et al., 2006; Mancini et al., 2010; Afshari et al., 2012; Blauwkamp et al., 2019). Such methods fail to identify a pathogen as often as 50% of the time (Srinivasan et al., 2015). Situations where culture- based methods fail to identify pathogenic organisms in cases involving infection (culture- negative infections), have been shown to increase the risk of further complications due to uncertainties involving identification of pathogen(s) and associated resistances, which can delay the proper treatment required.

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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.

There has been no prior application of matched metagenomics and meta-transcriptomics in Clostridioides difficile infection (CDI) evaluating the role of fungi in CDI or identifying community functions that contribute to the development of this disease. We collected diarrheal stools from inpatients and utilized a tiered sequencing approach to identify enriched bacterial and fungal taxa, using 16S and internal transcribed spacer (ITS) rRNA gene amplicon sequencing, with matched metagenomics and metatranscriptomics performed on a subset of the population. Distinct bacterial and fungal compositions distinguished CDI-positive and -negative patients, with the greatest differentiation between the cohorts observed based on bacterial metatranscriptomics. Bipartite network analyses demonstrated that Aspergillus and Penicillium taxa shared a strong positive relationship in CDI patients and together formed negative co-occurring relationships with several bacterial taxa.

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