Segmental Colitis Associated With Diverticulosis.

Segmental colitis associated with diverticulosis (SCAD) is a rare entity characterized by segmental circumferential colonic wall thickening involving the sigmoid and/or left colon in the presence of colonic diverticulosis. We present the case of a 57-year-old female with a past medical history of colonic diverticulosis who presented with chronic intermittent abdominal pain, non-bloody diarrhea, and hematochezia. Imaging revealed long-segment circumferential colonic wall thickening involving the sigmoid and distal descending colon with engorged vasa recta without significant inflammation around the colon or diverticula, consistent with SCAD. Colonoscopy showed diffuse mucosal edema and hyperemia of the descending and sigmoid colon with easy friability and erosions primarily affecting the inter-diverticular colonic mucosa. Pathology showed changes of chronic colitis including inflammation in the lamina propria, crypt distortion, and granuloma formation. Treatment with antibiotics and mesalamine was initiated with improvement in symptoms. This case highlights the importance of considering segmental colitis associated with diverticulosis in patients with chronic lower abdominal pain and diarrhea in the setting of colonic diverticulosis, and the need for a thorough workup including imaging, colonoscopy, and histopathology to differentiate it from other types of colitis.

Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters.

Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning-based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.