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Background: Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression., Methods: We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics., Results: Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%)., Conclusions: Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups., (© 2024. The Author(s).)

Ovine congenital progressive muscular dystrophy (OCPMD) was first described in Merino sheep flocks in Queensland and Western Australia in the 1960s and 1970s. The most prominent feature of the disease is a distinctive gait with stiffness of the hind limbs that can be seen as early as 3 weeks after birth. The disease is progressive. Histopathological examination had revealed dystrophic changes specifically in type I (slow) myofibres, while electron microscopy had demonstrated abundant nemaline bodies. Therefore, it was never certain whether the disease was a dystrophy or a congenital myopathy with dystrophic features. In this study, we performed whole genome sequencing of OCPMD sheep and identified a single base deletion at the splice donor site (+ 1) of intron 13 in the type I myofibre-specific TNNT1 gene (KT218690 c.614 + 1delG). All affected sheep were homozygous for this variant. Examination of TNNT1 splicing by RT-PCR showed intron retention and premature termination, which disrupts the highly conserved 14 amino acid C-terminus. The variant did not reduce TNNT1 protein levels or affect its localization but impaired its ability to modulate muscle contraction in response to Ca 2+ levels. Identification of the causative variant in TNNT1 finally clarifies that the OCPMD sheep is in fact a large animal model of TNNT1 congenital myopathy. This model could now be used for testing molecular or gene therapies.