Immunization of two cynomolugus and three rhesus monkeys with purified type II collagen resulted in the development of polyarthritis. Arthritis first became clinically apparent 7 wk after primary immunization and persisted for 16 mo. Radiologic examination of the limbs demonstrated soft tissue swelling with severe joint destruction including loss of cartilage and bone. Involved joints eventually became ankylosed with permanent loss of some motion. All of the monkeys developed a response to the immunizing collagen as determined by ELISA of serum for antibodies. Arthritis was associated with weight loss and constitutional symptoms, including lethargy and refusal to eat. One monkey became so debilitated that it was necessary to euthanize it. Histologic examination of the joints showed synovial hypertrophy with pannus formation. A control monkey immunized with type I collagen suffered no apparent ill effects.

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Crohn’s disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

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