Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans - GSE33341 - GPL571

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 95 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.82). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.94, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

To create a host gene expression-based classifier for S. aureus infection, mice from a variety of experimental conditions were utilized. Seven different strains of inbred mice (n=187 total) were challenged with four different S. aureus strains via intraperitoneal inoculation and sacrificed at various time points as described in Methods. The comparator group for model derivation included 50 A/J or C57BL/6J mice inoculated with E. coli (O18:K1:H7) as well as 54 uninfected mice. Next, the murine S. aureus classifier was externally validated in outbred CD-1 mice with S. aureus infection (Sanger 476 or USA300), E. coli infection (O18:K1:H7), or uninfected controls (10 animals per condition). Method: All experiments were performed on mice 6-8 weeks old. For the murine S. aureus predictor, seven inbred mouse strains (3 mice/strain: 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, C57BL/6J, C3H/HeJ, and NOD/LtJ) were IP inoculated with 107 CFU/g of S. aureus Sanger476, euthanized at 2h after injection, and bled. This was repeated using four different S. aureus strains (USA100, USA300, MW2, and Sanger476) in A/J mice (n=3 per S. aureus strain). For time series experiments, both A/J and C57BL/6J mouse strains were IP inoculated with S. aureus Sanger476 as above, and sacrificed at 2, 4, 6, and 12h after injection (n=5 per time point).

Staphylococcus aureus (2021 ICD-10-CM code* = A41.0)

Escherichia coli blood Infection (2021 ICD-10-CM code* = A41.5)

Data processing was conducted using the Robust Multichip Average (RMA) generated by Affymetrix Expression Console software. Microarray data was analyzed in two steps following the analysis strategy. First, a Bayesian sparse factor model was fit to the expression data without regard to phenotype. Second, factors were then used as independent variables to build a penalized binary regression with variable selection model trained to identify S. aureus infection. In order to minimize issues with overfitting, batch was not included in the regression models. This approach also allows for model averaging, which properly accounts for uncertainty in the choice of predictors and typically outperforms the single best model in predictive accuracy. Genes were filtered for analysis using non-specific filtering for genes with high mean expression and high variance across samples. Samples with a high number of outlying genes were removed during the factor analysis. Mice were batched into discrete experiments with each experiment containing the relevant controls to avoid confounding. Using the same murine experimental data, another classifier was derived to classify methicillin-resistant vs. methicillin-sensitive S. aureus infection. The methodology was otherwise the same as that described above. We fit a factor model on the human data independently from the mouse data. The factor model was fit to 8,892 genes after non-specific filtering to remove unexpressed and uniformly expressed genes. The factor model was trained on the 95 samples from three batches of expression data, and this resulted in 77 factors. These 77 factors were then projected onto the full data set with the goal of distinguishing S. aureus BSI from healthy controls or E. coli BSI. Leave-one-out cross-validation was utilized in order to control for overfitting of the penalized binary regression model. In order to minimize issues with overfitting, batch was not included in the regression models. Matlab scripts to perform these operations are available.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33341