Putative mRNA Biomarkers for the Eradication of Infection in an Equine Experimental Model of Septic Arthritis.
Roman V KoziyJosé L BracamonteGeorge S KatselisDaniel UdenzeShahina HayatS Austin HammondElemir SimkoPublished in: Veterinary sciences (2024)
Septic arthritis (SA) in horses has long-term health implications. The success of its resolution hinges on the implementation of early, aggressive treatment, which is often sustained over a prolonged period. Common diagnostic methods do not allow for the reliable detection of the eradication of joint infection. A potential alternative is the discovery and characterization of mRNA biomarkers. The purpose of this study was to identify potential mRNA biomarkers for the eradication of joint infection in equine SA and to compare their expression with our previously published proteomics data. In addition, the transcriptomics data were compared to the mRNA biomarker panel, SeptiCyte Lab, used to distinguish sepsis from non-septic shock in humans. A comparative transcriptomics analysis of synovial fluid from the SA joints of five horses with active infection and subsequent post-treatment eradicated infection in the same joints and five horses with non-septic synovitis was performed. Eight novel mRNA transcripts were identified that were significantly upregulated (>3-fold) in horses with active SA compared to horses post-eradication of infection after treatment and horses with non-septic synovitis. Two proteins in our proteomics data corresponded to these mRNA transcripts, but were not statistically different. The transcripts used in the SeptiCyte test were not differentially expressed in our study. Our results suggest that mRNA may be a useful source of biomarkers for the eradication of joint infection in horses and warrants further investigation.
Keyphrases
- acute kidney injury
- binding protein
- septic shock
- helicobacter pylori infection
- healthcare
- rheumatoid arthritis
- mass spectrometry
- electronic health record
- public health
- single cell
- primary care
- small molecule
- big data
- mental health
- intensive care unit
- poor prognosis
- randomized controlled trial
- systematic review
- human health
- label free
- high throughput
- artificial intelligence
- deep learning