Transcriptomic analysis of equine chorioallantois reveals immune networks and molecular mechanisms involved in nocardioform placentitis.

Nocardioform placentitis (NP) continues to result in episodic outbreaks of abortion and preterm birth in mares and remains a poorly understood disease. The objective of this study was to characterize the transcriptome of the chorioallantois (CA) of mares with NP. The CA were collected from mares with confirmed NP based upon histopathology, microbiological culture and PCR for Amycolatopsis spp. Samples were collected from the margin of the NP lesion (NPL, n = 4) and grossly normal region (NPN, n = 4). Additionally, CA samples were collected from normal postpartum mares (Control; CRL, n = 4). Transcriptome analysis identified 2892 differentially expressed genes (DEGs) in NPL vs. CRL and 2450 DEGs in NPL vs. NPN. Functional genomics analysis elucidated that inflammatory signaling, toll-like receptor signaling, inflammasome activation, chemotaxis, and apoptosis pathways are involved in NP. The increased leukocytic infiltration in NPL was associated with the upregulation of matrix metalloproteinase (MMP1, MMP3, and MMP8) and apoptosis-related genes, such as caspases (CASP3 and CASP7), which could explain placental separation associated with NP. Also, NP was associated with downregulation of several placenta-regulatory genes (ABCG2, GCM1, EPAS1, and NR3C1), angiogenesis-related genes (VEGFA, FLT1, KDR, and ANGPT2), and glucose transporter coding genes (GLUT1, GLUT10, and GLUT12), as well as upregulation of hypoxia-related genes (HIF1A and EGLN3), which could elucidate placental insufficiency accompanying NP. In conclusion, our findings revealed for the first time, the key regulators and mechanisms underlying placental inflammation, separation, and insufficiency during NP, which might lead to the development of efficacious therapies or diagnostic aids by targeting the key molecular pathways.

Application of an automated surveillance-data-analysis system in a laboratory-based early-warning system for detection of an abortion outbreak in mares.

OBJECTIVE: To develop an early-warning automated surveillance-data-analysis system for early outbreak detection and reporting and to assess its performance on an abortion outbreak in mares in Kentucky. SAMPLE POPULATION: 426 data sets of abortions in mares in Kentucky during December 2000 to July 2001. PROCEDURES: A custom software system was developed to automatically extract and analyze data from a Laboratory Information Management System database. The software system was tested on data on abortions in mares in Kentucky reported between December 1, 2000, and July 31, 2001. The prospective space-time permutations scan statistic, proposed by Kulldorff, was used to detect and identify abortion outbreak signals. RESULTS: Results indicated that use of the system would have detected the abortion outbreak approximately 1 week earlier than traditional surveillance systems. However, the geographic scale of analysis was critical for highest sensitivity in outbreak detection. Use of the lower geographic scale of analysis (ie, postal [zip code]) enhanced earlier detection of significant clusters, compared with use of the higher geographic scale (ie, county). CONCLUSIONS AND CLINICAL RELEVANCE: The automated surveillance-data-analysis system would be useful in early detection of endemic, emerging, and foreign animal disease outbreaks and might help in detection of a bioterrorist attack. Manual analyses of such a large number of data sets (ie, 426) with a computationally intensive algorithm would be impractical toward the goal of achieving near real-time surveillance. Use of this early-warning system would facilitate early interventions that should result in more positive health outcomes.