Rodent models have been extensively used to investigate the cause and mechanisms behind Alzheimer’s disease. Despite many years of intensive research using these models we still lack a detailed understanding of the molecular events that lead to neurodegeneration. Although zebrafish lack the complexity of advanced cognitive behaviors evident in rodent models they have proven to be a very informative model for the study of human diseases. In this review we give an overview of how the zebrafish has been used to study Alzheimer’s disease. Zebrafish possess genes orthologous to those mutated in familial Alzheimer’s disease and research using zebrafish has revealed unique characteristics of these genes that have been difficult to observe in rodent models. The zebrafish is becoming an increasingly popular model for the investigation of Alzheimer’s disease and will complement studies using other models to help complete our understanding of this disease.

Serum levels of miR-194 have been reported to predict prostate cancer recurrence after surgery, but its functional contributions to this disease have not been studied. Herein, it is demonstrated that miR-194 is a driver of prostate cancer metastasis. Prostate tissue levels of miR-194 were associated with disease aggressiveness and poor outcome. Ectopic delivery of miR-194 stimulated migration, invasion, and epithelial–mesenchymal transition in human prostate cancer cell lines, and stable overexpression of miR-194 enhanced metastasis of intravenous and intraprostatic tumor xenografts. Conversely, inhibition of miR-194 activity suppressed the invasive capacity of prostate cancer cell lines in vitro and in vivo. Mechanistic investigations identified the ubiquitin ligase suppressor of cytokine signaling 2 (SOCS2) as a direct, biologically relevant target of miR-194 in prostate cancer. Low levels of SOCS2 correlated strongly with disease recurrence and metastasis in clinical specimens. SOCS2 downregulation recapitulated miR-194–driven metastatic phenotypes, whereas overexpression of a nontargetable SOCS2 reduced miR-194–stimulated invasion. Targeting of SOCS2 by miR-194 resulted in derepression of the oncogenic kinases FLT3 and JAK2, leading to enhanced ERK and STAT3 signaling. Pharmacologic inhibition of ERK and JAK/STAT pathways reversed miR-194–driven phenotypes. The GATA2 transcription factor was identified as an upstream regulator of miR-194, consistent with a strong concordance between GATA2 and miR-194 levels in clinical specimens. Overall, these results offer new insights into the molecular mechanisms of metastatic progression in prostate cancer. Cancer Res; 77(4); 1021–34. ©2016 AACR.

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.