Abstract
Wesselsbron virus (WSLV) disease is an important neglected cause of hepatitis in ruminants with potential for zoonotic transmission, yet its histological lesions have been scarcely studied. We performed a thorough machine learning-driven, pathologist-led digital histopathological assessment of WSLV-induced hepatitis in ewes and lambs infected with clade I (rSA999) (n = 6) or clade II (SAH117) (n = 8) strains and a mock group (n = 6). The analysis was performed on immunohistochemical (IHC) staining for T cells (CD3), B cells (PAX5), histiocytes (Iba1), the WSLV nonstructural protein 1 (NS1), and a double stain for arginase 1 and Ki67 to assess the hepatocyte proliferation index (PI). WSLV-infected animals exhibited significantly higher lymphohistiocytic infiltration and higher hepatocyte PI compared to the mock group. The T cell density was 10 folds higher than the B cell density and was more pronounced in the rSA999 group than in the SAH177 group. Digitally quantified parameters positively correlated with WSLV reverse transcription quantitative PCR (RT-qPCR) results and hepatic injury markers (aspartate transferase [AST], bilirubin, and adenosine deaminase [ADA]), indicating that digital histopathology reliably detects liver damage and disease severity. Among the parameters assessed, the positive correlation between the density of Iba1+ staining and the WSLV viral load in the liver was the strongest, underscoring the prominent involvement of histiocytes in WSLV-induced hepatitis. This study demonstrates the value of digital histopathological analysis in viral-induced hepatitis using formalin-fixed paraffin-embedded (FFPE) tissue, leveraging whole-slide imaging and deep learning (DL) to objectively characterize key hepatic alterations caused by the viral infection.