# Integrated Bioinformatics Analysis for Target Identification and Evaluation of Recombinant Protein as an Antigen for Intradermal Skin Test in Bovine Tuberculosis Diagnosis

## TL;DR

This study identifies a new protein antigen for diagnosing bovine tuberculosis using bioinformatics and in vivo testing.

## Contribution

A novel pipeline for antigen selection in bovine tuberculosis diagnosis using integrated bioinformatics and recombinant protein evaluation.

## Key findings

- A pangenome analysis identified 12 unique genes specific to M. bovis SP38.
- The recombinant HP28 protein induced a 6.6 mm skin reaction in guinea pigs, showing potential as a diagnostic antigen.
- Integrated bioinformatics and in vivo testing revealed a promising approach for improving bTB diagnostics.

## Abstract

Bovine
tuberculosis (bTB) is a respiratory disease caused
by Mycobacterium bovis, posing a significant
threat
to animal health and the livestock industry. Current control strategies
for bTB rely on diagnostic tests and slaughter policies. However,
the limitations of existing diagnostic methods, which depend on PPD
antigens, necessitate the exploration of alternative antigens to enhance
the accuracy and reliability of bTB diagnosis. This study aimed to
identify, produce, and evaluate novel antigens for use in the intradermal
skin test for bTB diagnosis. A pangenome analysis of four Mycobacterium species identified 12 unique genes
specific to M. bovis SP38. Further
integrated bioinformatic analysis revealed 224 genomic islands associated
with virulence and pathogenesis. Among these, a highly antigenic protein,
termed HP28, was selected for in vivo testing. The recombinant HP28
protein (rHP28) was expressed in E. coli and assessed for its ability to induce intradermal skin reactions
in guinea pigs. The rHP28 protein elicited a skin reaction of 6.6
mm at 72 h post-injection, whereas negative controls showed no reaction.
This study presents a pipeline for the selection of antigens using
integrated bioinformatic analysis to identify diagnostic targets that
can effectively distinguish between sensitized and non-sensitized
animals, offering a promising approach for improving bTB diagnostics.