Machine learning-based identification of a transcriptomic blood signature discriminating between systemic autoimmunity and infection.

Background: Pathogenic responses against self and foreign antigens in systemic autoimmunity and infection, respectively, engage similar immunologic components, thus lacking distinguishing diagnostic biomarkers. Herein, we tested whether whole-blood transcriptome analysis discriminates autoimmune from infectious diseases.

Methods: We applied nested cross-validation methodology to tune and validate random forests, k-nearest neighbors, and support vector machines, using a new preprocessing method on 22 publicly available datasets, including 594 patients with a broad spectrum of systemic autoimmune diseases and 615 patients with diverse viral, bacterial, and parasitic infections.

Findings: Our preprocessing method tackled RNA sequencing batch effects by sorting the genes within each sample according to individual relative expression values and discriminated between the corresponding pathologies with 98% accuracy versus 63% when using raw values. This model was further tested in external datasets comprising various autoimmune diseases and infections new to its training process, yielding accuracies ranging between 80% and 96%. Enrichment analyses of 457 of the most informative genes identified SAP1, ELF1/4, and FLI1 transcription factors among the significant upstream regulators and revealed several key processes and pathways, such as autophagy, DNA damage response, and NOTCH signaling. A subset of 24 genes, including the inflammation-related genes RPL7, TLK2, and ANK2, distinguished between autoimmune and infectious diseases with 89% accuracy.

Conclusions: Using a novel batch-correction algorithm, this analysis may provide a new mechanistic understanding of the pathogenic autoimmune response, as well as biomarkers for differential diagnoses of the corresponding pathologies in patients presenting with inflammatory disorders.

Funding: This work was funded by the European Regional Development Fund NSRF 2014-2020, no. MIS5002802.