Inter-individual variance in host immune responses following vaccination can result in failure to develop protective immunity leaving individuals at risk for infection in addition to compromising herd immunity. While developing more efficacious vaccines is one strategy to mitigate this problem, predicting vaccine responsiveness prior to vaccination could inform which individuals require adjunct disease management strategies. To identify biomarkers of vaccine responsiveness, a cohort of pigs (n = 120) were vaccinated and pigs representing the high (n = 6; 90th percentile) and low (n = 6; 10th percentile) responders based on vaccine-specific antibody responses following vaccination were further analyzed. Kinase-mediated phosphorylation events within peripheral blood mononuclear cells collected prior to vaccination identified 53 differentially phosphorylated peptides when comparing low responders with high responders. Functional enrichment analysis revealed pro-inflammatory cytokine signaling pathways as dysregulated, and this was further substantiated by detection of higher (p < 0.01) concentrations of interferon-gamma in plasma of low responders compared to high responders prior to vaccination. In addition, low responder pigs with high plasma interferon-gamma showed lower (p < 0.01) birth weights than high responder pigs. These associations between vaccine responsiveness, cytokine signaling within peripheral immune cells, and body weight in pigs provide both evidence and insight into potential biomarkers for identifying low responders to vaccination.

Cytogenetic aberrations at the single-cell level represent an important characteristic of cancer cells relevant to tumor evolution and prognosis. However, with the advent of The Cancer Genome Atlas (TCGA), there has been a major shift in cancer research to the use of data from aggregate cell populations. Given that tumor cells harbor hundreds to thousands of biologically relevant genetic alterations that manifest as intratumor heterogeneity, these aggregate analyses may miss alterations readily observable at single-cell resolution. Using the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, we developed an algorithm to parse International System for Cytogenetic Nomenclature notation for quantitative abnormalities. Comparison of the Mitelman database and TCGA demonstrated that the Mitelman database is a powerful resource, and that cytogenetic aberrations captured by traditional approaches used in Mitelman database are on par with population-based genomic analyses used in TCGA. This algorithm will help nonspecialists to overcome the challenges associated with the format and syntax of the Mitelman database.Significance: A novel in silico approach compares cytogenetic data between the Mitelman database and TCGA, highlighting the advantages and limitations of both datasets.

Within human health research, the remarkable utility of kinase inhibitors as therapeutics has motivated efforts to understand biology at the level of global cellular kinase activity (the kinome). In contrast, the diminished potential for using kinase inhibitors in food animals has dampened efforts to translate this research approach to livestock species. This, in our opinion, was a lost opportunity for livestock researchers given the unique potential of kinome analysis to offer insight into complex biology. To remedy this situation, our lab developed user-friendly, cost-effective approaches for kinome analysis that can be readily incorporated into most research programs but with a specific priority to enable the technology to livestock researchers. These contributions include the development of custom software programs for the creation of species-specific kinome arrays as well as comprehensive deconvolution and analysis of kinome array data. Presented in this review are examples of the application of kinome analysis to highlight the utility of the technology to further our understanding of two key complex biological events of priority to the livestock industry: host immune responses to infectious diseases and animal stress responses. These advances and examples of application aim to provide both mechanisms and motivation for researchers, particularly livestock researchers, to incorporate kinome analysis into their research programs.

the mite Varroa destructor is a serious threat to honeybee populations. Selective breeding for Varroa mite tolerance could be accelerated by biomarkers within individual bees that could be applied to evaluate a colony phenotype. Previously, we demonstrated differences in kinase-mediated signaling between bees from colonies of extreme phenotypes of mite susceptibility. We expand these findings by defining a panel of 19 phosphorylation events that differ significantly between individual pupae from multiple colonies with distinct Varroa mite tolerant phenotypes. the predictive capacity of these biomarkers was evaluated by analyzing uninfested pupae from eight colonies representing a spectrum of mite tolerance. The pool of biomarkers effectively discriminated individual pupae on the basis of colony susceptibility to mite infestation. Kinome analysis of uninfested pupae from mite tolerant colonies highlighted an increased innate immune response capacity. The implication that differences in innate immunity contribute to mite susceptibility is supported by the observation that induction of innate immune signaling responses to infestation is compromised in pupae of the susceptible colonies. collectively, biomarkers within individual pupae that are predictive of the susceptibility of colonies to mite infestation could provide a molecular tool for selective breeding of tolerant colonies.Prioritizing bees at the dark-eyed pupae stage of development minimizes potential signaling variability arising from different castes or environmental influences in adult bees. All samples were analyzed within the same kinome assay to minimize technical effects due to inter-assay variability. Individual frozen pupae were placed in a sealed plastic bag in the presence of 300 μl of lysis buffer 25 . The pupae were pulverized with a rubber mallet and the resulting suspension was centrifuged at 10,000 × g for 10 min. Supernatants were used for kinome analysis 25 . Data analysis. The dataset for each array contains signal intensities associated with the nine technical replicates for each of the 299 peptides spotted on each array. Kinome data were processed through PIIKA 2, a pipeline for processing kinome array data 45 .Identification of peptide biomarkers of varroa mite susceptibility. A one-sided paired student's t-test of normal distribution was used to compare normalized signal intensity values for each of the 299 peptides of individual pupae (n = 5) representing the two high and low colony phenotypes of Varroa mite susceptibility. Specifically, mite tolerance was represented by pupae from the S88 (n = 3) and S23A (n = 2) colonies while mite susceptibility was represented by pupae from the G4 (n = 3) and S88-4 (n = 2) colonies in the absence of mite infestation. Peptides with significant (P-value < 0.01) differences in levels of phosphorylation between pupae of the two phenotypes were classified as potential biomarkers.

Peptide microarrays consisting of defined phosphorylation target sites are an effective approach for high throughput analysis of cellular kinase (kinome) activity. Kinome peptide arrays are highly customizable and do not require species-specific reagents to measure kinase activity, making them amenable for kinome analysis in any species. Our group developed software, Platform for Integrated, Intelligent Kinome Analysis (PIIKA), to enable more effective extraction of meaningful biological information from kinome peptide array data. A subsequent version, PIIKA2, unveiled new statistical tools and data visualization options. Here we introduce PIIKA 2.5 to provide two essential quality control metrics and a new background correction technique to increase the accuracy and consistency of kinome results. The first metric alerts users to improper spot size and informs them of the need to perform manual resizing to enhance the quality of the raw intensity data. The second metric uses inter-array comparisons to identify outlier arrays that sometimes emerge as a consequence of technical issues. In addition, a new background correction method, background scaling, can sharply reduce spatial biases within a single array in comparison to background subtraction alone. Collectively, the modifications of PIIKA 2.5 enable identification and correction of technical issues inherent to the technology and better facilitate the extraction of meaningful biological information. We show that these metrics demonstrably enhance kinome analysis by identifying low quality data and reducing batch effects, and ultimately improve clustering of treatment groups and enhance reproducibility. The web-based and stand-alone versions of PIIKA 2.5 are freely accessible at via http://saphire.usask.ca.