Molecular Profiling of Axial Spondyloarthritis Patients Reveals an Association between Innate and Adaptive Cell Populations and Therapeutic Response to Tumor Necrosis Factor Inhibitors.

This study aims at identifying molecular biomarkers differentiating responders and non-responders to treatment with Tumor Necrosis Factor inhibitors (TNFi) among patients with axial spondyloarthritis (axSpA). Whole blood mRNA and plasma proteins were measured in a cohort of biologic-naïve axSpA patients (n = 35), pre and post (14 weeks) TNFi treatment with adalimumab. Differential expression analysis was used to identify the most enriched pathways and in predictive models to distinguish responses to TNFi. A treatment-associated signature suggests a reduction in inflammatory activity. We found transcripts and proteins robustly differentially expressed between baseline and week 14 in responders. C-reactive protein (CRP) and Haptoglobin (HP) proteins showed strong and early decrease in the plasma of axSpA patients, while a cluster of apolipoproteins (APOD, APOA2, APOA1) showed increased expression at week 14. Responders to TNFi treatment present higher levels of markers of innate immunity at baseline, and lower levels of adaptive immunity markers, particularly B-cells. A logistic regression model incorporating ASDAS-CRP, gender, and AFF3, the top differentially expressed gene at baseline, enabled an accurate prediction of response to adalimumab in our cohort (AUC = 0.97). In conclusion, innate and adaptive immune cell type composition at baseline may be a major contributor to response to adalimumab in axSpA patients. A model including clinical and gene expression variables should also be considered.

Mass Spectrometry-Based Proteomic and Metabolomic Profiling of Serum Samples for Discovery and Validation of Tuberculosis Diagnostic Biomarker Signature.

Tuberculosis (TB) is a transmissible disease listed as one of the 10 leading causes of death worldwide (10 million infected in 2019). A swift and precise diagnosis is essential to forestall its transmission, for which the discovery of effective diagnostic biomarkers is crucial. In this study, we aimed to discover molecular biomarkers for the early diagnosis of tuberculosis. Two independent cohorts comprising 29 and 34 subjects were assayed by proteomics, and 49 were included for metabolomic analysis. All subjects were arranged into three experimental groups­healthy controls (controls), latent TB infection (LTBI), and TB patients. LC-MS/MS blood serum protein and metabolite levels were submitted to univariate, multivariate, and ROC analysis. From the 149 proteins quantified in the discovery set, 25 were found to be differentially abundant between controls and TB patients. The AUC, specificity, and sensitivity, determined by ROC statistical analysis of the model composed of four of these proteins considering both proteomic sets, were 0.96, 93%, and 91%, respectively. The five metabolites (9-methyluric acid, indole-3-lactic acid, trans-3-indoleacrylic acid, hexanoylglycine, and N-acetyl-L-leucine) that better discriminate the control and TB patient groups (VIP > 1.75) from a total of 92 metabolites quantified in both ionization modes were submitted to ROC analysis. An AUC = 1 was determined, with all samples being correctly assigned to the respective experimental group. An integrated ROC analysis enrolling one protein and four metabolites was also performed for the common control and TB patients in the proteomic and metabolomic groups. This combined signature correctly assigned the 12 controls and 12 patients used only for prediction (AUC = 1, specificity = 100%, and sensitivity = 100%). This multiomics approach revealed a biomarker signature for tuberculosis diagnosis that could be potentially used for developing a point-of-care diagnosis clinical test.