Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle

Abstract

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of ‘-omics’ data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.

Non‐coding and coding genomic variants distinguish prostate cancer, castration‐resistant prostate cancer, familial prostate cancer, and metastatic castration‐resistant prostate cancer from each other

“…Compared with analyses of single variant associations, it is probable that the combined coding and non‐coding variants indicate the risk of prostate cancer more accurately. Advances in application of machine learning models such as decision tree algorithms, attribute weighting models, association rule mining, SVM, and neural networks and more importantly, integration of machine learning and meta‐analysis provides a new platform for biomarker discovery. It should be noted that racial and ethnic differences exist in advanced prostate cancer where multiple combinations of variants can lead to castration‐resistant prostate cancer and metastatic castration‐resistant prostate cancer.…”

The use of the Heter-LP algorithm to reposition antibiotics for managing E. coli mastitis in dairy cattle

“…Key genes that had a robust bio-signature in response to mastitis specially in E. coli infection. These genes are the results of our recent research ndings based on meta-analyses to detect up/down regulated genes by utilizing machine learning algorithms [1] and network analysis [2,28], 3. Functionally related diseases or biological processes related to bovine mastitis illustrated by Pathway Studio web tool.…”

“…Previously we identify differentially expressed genes in response to E. coli mastitis [1,2,28]. These genes have a robust bio-signature and thereby may be useful biomarker or therapeutic target candidates in mastitis [1,2]. These genes/proteins are listed in Table 2 and added to the disease-gene relation part of the dataset shown in Table 1.…”

“…The therapeutic success of bovine mastitis depends mainly on accurate diagnosis of kind of pathogen, it will contribute to improvement of clinical and microbiological e cacy and helps to prevent of emergence and spread of resistant microorganisms. Despite the development prospects for bovine mastitis diagnosis with a focus on speci c pathogens at an early stage fast together with the e cient devices can offer a "cow-side" and "on-site" with high sensitivity and speci city [1,6,[15][16][17]; the most e cient, safe, and economical treatments for mastitis are still topics of scienti c debate [6,18,19]. Generally, narrow and/or broad spectrum antibiotics are generally used for the treatment of E. coli mastitis, but treatment studies have shown controversial results.…”

Optimal Length of R-R Interval Segment Window for Lorenz Plot Detection of Paroxysmal Atrial Fibrillation by Machine Learning

“…Recently, machine learnings have become popular in medical elds [19]. For LP-based AF detection, we used a convolutional neural network (CNN) suitable for image discrimination.…”