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Triple-negative breast cancer (TNBC) tumours that lack expression of oestrogen, and progesterone receptors, and do not overexpress the HER2 receptor represent the most aggressive breast cancer subtype, which is characterised by the resistance to therapy in frequently relapsing tumours and a high rate of patient mortality. This is likely due to the resistance of slowly proliferating tumour-initiating cells (TICs), and understanding molecular mechanisms that control TICs behaviour is crucial for the development of effective therapeutic approaches. Here, we present our novel findings, indicating that an intrinsically catalytically inactive member of the Eph group of receptor tyrosine kinases, EPHB6, partially suppresses the epithelial–mesenchymal transition in TNBC cells, while also promoting expansion of TICs. Our work reveals that EPHB6 interacts with the GRB2 adapter protein and that its effect on enhancing cell proliferation is mediated by the activation of the RAS-ERK pathway, which allows it to elevate the expression of the TIC-related transcription factor, OCT4. Consistent with this, suppression of either ERK or OCT4 activities blocks EPHB6-induced pro-proliferative responses. In line with its ability to trigger propagation of TICs, EPHB6 accelerates tumour growth, potentiates tumour initiation and increases TIC populations in xenograft models of TNBC. Remarkably, EPHB6 also suppresses tumour drug resistance to DNA-damaging therapy, probably by forcing TICs into a more proliferative, drug-sensitive state. In agreement, patients with higher EPHB6 expression in their tumours have a better chance for recurrence-free survival. These observations describe an entirely new mechanism that governs TNBC and suggest that it may be beneficial to enhance EPHB6 action concurrent with applying a conventional DNA-damaging treatment, as it would decrease drug resistance and improve tumour elimination.

Background Gene set analysis is a well-established approach for interpretation of data from high-throughput gene expression studies. Achieving reproducible results is an essential requirement in such studies. One factor of a gene expression experiment that can affect reproducibility is the choice of sample size. However, choosing an appropriate sample size can be difficult, especially because the choice may be method-dependent. Further, sample size choice can have unexpected effects on specificity. Results In this paper, we report on a systematic, quantitative approach to study the effect of sample size on the reproducibility of the results from 13 gene set analysis methods. We also investigate the impact of sample size on the specificity of these methods. Rather than relying on synthetic data, the proposed approach uses real expression datasets to offer an accurate and reliable evaluation. Conclusion Our findings show that, as a general pattern, the results of gene set analysis become more reproducible as sample size increases. However, the extent of reproducibility and the rate at which it increases vary from method to method. In addition, even in the absence of differential expression, some gene set analysis methods report a large number of false positives, and increasing sample size does not lead to reducing these false positives. The results of this research can be used when selecting a gene set analysis method from those available.

Background Clubroot is an important disease caused by the obligate parasite Plasmodiophora brassicae that infects the Brassicaceae. As a soil-borne pathogen, P. brassicae induces the generation of abnormal tissue in the root, resulting in the formation of galls. Root infection negatively affects the uptake of water and nutrients in host plants, severely reducing their growth and productivity. Many studies have emphasized the molecular and physiological effects of the clubroot disease on root tissues. The aim of the present study is to better understand the effect of P. brassicae on the transcriptome of both shoot and root tissues of Arabidopsis thaliana. Results Transcriptome profiling using RNA-seq was performed on both shoot and root tissues at 17, 20 and 24 days post inoculation (dpi) of A. thaliana, a model plant host for P. brassicae. The number of differentially expressed genes (DEGs) between infected and uninfected samples was larger in shoot than in root. In both shoot and root, more genes were differentially regulated at 24 dpi than the two earlier time points. Genes that were highly regulated in response to infection in both shoot and root primarily were involved in the metabolism of cell wall compounds, lipids, and shikimate pathway metabolites. Among hormone-related pathways, several jasmonic acid biosynthesis genes were upregulated in both shoot and root tissue. Genes encoding enzymes involved in cell wall modification, biosynthesis of sucrose and starch, and several classes of transcription factors were generally differently regulated in shoot and root. Conclusions These results highlight the similarities and differences in the transcriptomic response of above- and below-ground tissues of the model host Arabidopsis following P. brassicae infection. The main transcriptomic changes in root metabolism during clubroot disease progression were identified. An overview of DEGs in the shoot underlined the physiological changes in above-ground tissues following pathogen establishment and disease progression. This study provides insights into host tissue-specific molecular responses to clubroot development and may have applications in the development of clubroot markers for more effective breeding strategies. Electronic supplementary material The online version of this article (10.1186/s12864-017-4426-7) contains supplementary material, which is available to authorized users.

We describe immune-proteome structures using libraries of protein fragments that define a structural immunological alphabet. We propose and validate such an alphabet as i) composed of letters of five consecutive amino acids, pentapeptide units being sufficient minimal antigenic determinants in a protein, and ii) characterized by low-similarity to human proteins, so representing structures unknown to the host and potentially able to evoke an immune response. In this context, we have thoroughly sifted through the entire human proteome searching for non-redundant protein motifs. Here, for the first time, a complete sequence redundancy dissection of the human proteome has been conducted. The non-redundant peptide islands in the human proteome have been quantified and catalogued according to the amino acid length. The library of uniquely occurring n-peptide sequences that was obtained is characterized by a logarithmic decrease of the number of non-redundant peptides as a function of the peptide length. This library represents a highly specific catalogue of molecular protein signatures, the possible use of which in cancer/autoimmunity research is discussed, with a major focus on non-redundant dodecamer sequences.

Our labs are focused on identifying amino acid sequences having the ability to react specifically with the functional binding site of a complementary antibody. Our epitopic definition is based on the analysis of the similarity level of antigenic amino acid sequences to the host proteome. Here, the similarity profile to the human proteome of an HCV E1 immunodominant epitope, i.e. the HCV E1(315-328)HRMAWDMMMNWSPT sequence, led to i) characterizing the immunoreactive HCV E1 315-328 region as a sequence endowed with a low level of similarity to human proteins; ii) defining 2 contiguous immunodominant linear determinants respectively located at the NH(2) and COOH terminus of the conserved viral antigenic sequence. This study supports the hypothesis that low sequence similarity to the host's proteome modulates the pool of epitopic amino acid sequences in a viral antigen, and appears of potential value in defining immunogenic viral peptide sequences to be used in immunotherapeutic approaches for HCV treatment.

Major histocompatibility complex class I genes are among the most polymorphic genes characterized. The high level of polymorphism is essential for generating host immune responses. In humans, three distinct genomic loci encode human leukocyte antigen (HLA) class I genes, allowing individuals to express up to six different HLA class I molecules. In cattle, the number of distinct genomic loci are currently at least six, and the number of different bovine leukocyte antigens (BoLA) class I molecules that are expressed in individual animals are variable. The extent of allele variation within the cattle population is unknown. In this study, the number and variety of BoLA class I sequences expressed by 36 individuals were determined from full-length BoLA class I cDNA clones. Twenty distinct BoLA class I alleles were identified, with only four being previously reported. The number of expressed BoLA class I alleles in individual animals ranged between one and four, with none of the animals having an identical complement of BoLA class I molecules. Variation existed in the number of BoLA class I alleles expressed as well as the composition of expressed alleles, however, several BoLA class I alleles were found in multiple individual animals. Polymorphic amino acid sites were analyzed for positive and negative selection using the ADAPTSITE program. In the antigen recognition sites (ARS), there were eight positions that were predicted to be under positive selection and three positions that were predicted to be under negative selection from 62 positions. In contrast, for non-antigen recognition sites (non-ARS), there were three positions that were predicted to be under positive selection and 20 that were predicted to be under negative selection from 278, indicating that positive selection of amino acids occurs at a greater frequency within the antigen recognition sites.

Short amino acid motifs, either linear sequences or discontinuous amino acid groupings, can interact with specific protein domains, so exerting a central role in cell adhesion, signal transduction, hormone activity, regulation of transcript expression, enzyme activity, and antigen-antibody interaction. Here, we analyze the literature for such critical short amino acid motifs to determine the minimal peptide length involved in biologically important interactions. We report the pentapeptide unit as a common minimal amino acid sequence critically involved in peptide-protein interaction and immune recognition. The present survey may have implications in defining the dimensional module for peptide-based therapeutical approaches such as the development of novel antibiotics, enzyme inhibitors/activators, mimetic agonists/antagonists of neuropeptides, thrombolitic agents, specific anti-viral agents, etc. In such a therapeutical context, it is of considerable interest that low molecular weight peptides can easily cross biological barriers, are less susceptible to protease attacks, and can be administered at high concentrations. In addition, small peptides are a rational target for strategies aimed at antigen-specific immunotherapeutical intervention. As an example, specific short peptide fragments might be used to elicit antibodies capable of reacting with the full-length proteins containing the peptide fragment's amino acid sequence, so abolishing the risk of cross-reactivity.

Visualization is valuable in monitoring and debugging programs. The goal of the Wand research project at the University of Saskatchewan is to provide both a framework and tools for rapid development of visualization aids for logic programming languages. The ICOLA (Incremental Constraint-based Object Layout Algorithm) system is the newest graphics facility within Wand. ICOLA positions graphical objects according to object declarations and constraints specifying relative positional relationships among the objects. Three important features of ICOLA are that it is capable of creating reasonable pictures from highly under-constrained specifications, it uses an incremental constraint solution algorithm and hence generates those pictures efficiently, and it supports incremental (i.e. progressive) insertions and deletions of objects and constraints. The ability of the incremental algorithm to support such deletions is particularly noteworthy. This paper describes: PDI, the language supported by ICOLA; the incremental constraint solution algorithm itself; a successful implementation in Prolog and C; and results of a performance evaluation of the implementation.

Background Recently, questions have been raised regarding the ability of animal models to recapitulate human disease at the molecular level. It has also been demonstrated that cellular kinases, individually or as a collective unit (the kinome), play critical roles in regulating complex biology. Despite the intimate relationship between kinases and health, little is known about the variability, consistency and stability of kinome profiles across species and individuals. Results As a preliminary investigation of the existence of species- and individual-specific kinotypes (kinome signatures), peptide arrays were employed for the analysis of peripheral blood mononuclear cells collected weekly from human and porcine subjects (n = 6) over a one month period. The data revealed strong evidence for species-specific signalling profiles. Both humans and pigs also exhibited evidence for individual-specific kinome profiles that were independent of natural changes in blood cell populations. Conclusions Species-specific kinotypes could have applications in disease research by facilitating the selection of appropriate animal models or by revealing a baseline kinomic signature to which treatment-induced profiles could be compared. Similarly, individual-specific kinotypes could have implications in personalized medicine, where the identification of molecular patterns or signatures within the kinome may depend on both the levels of kinome diversity and temporal stability across individuals.

Background The increasing availability of whole genome sequences allows the gene or protein content of different organisms to be compared, leading to burgeoning interest in the relatively new subfield of pan-genomics. However, while several studies have analyzed protein content relationships in specific groups of bacteria, there has yet to be a study that provides a general characterization of protein content relationships in a broad range of bacteria. Results A variation on reciprocal BLAST hits was used to infer relationships among proteins in several groups of bacteria, and data regarding protein conservation and uniqueness in different bacterial genera are reported in terms of "core proteomes", "unique proteomes", and "singlets". We also analyzed the relationship between protein content similarity and the percent identity of the 16S rRNA gene in pairs of bacterial isolates from the same genus, and found that the strength of this relationship varied substantially depending on the genus, perhaps reflecting different rates of genome evolution and/or horizontal gene transfer. Finally, core proteomes and unique proteomes were used to study the proteomic cohesiveness of several bacterial species, revealing that some bacterial species had little cohesiveness in their protein content, with some having fewer proteins unique to that species than randomly-chosen sets of isolates from the same genus. Conclusions The results described in this study aid our understanding of protein content relationships in different bacterial groups, allowing us to make further inferences regarding genome-environment relationships, genome evolution, and the soundness of existing taxonomic classifications.

Background: Peptides derived from endogenous antigens can bind to MHC class I molecules. Those which bind with high affinity can invoke a CD8+ immune response, resulting in the destruction of infected cells. Much work in immunoinformatics has involved the algorithmic prediction of peptide binding affinity to various MHC-I alleles. A number of tools for MHC-I binding prediction have been developed, many of which are available on the web. Results: We hypothesize that peptides predicted by a number of tools are more likely to bind than those predicted by just one tool, and that the likelihood of a particular peptide being a binder is related to the number of tools that predict it, as well as the accuracy of those tools. To this end, we have built and tested a heuristic-based method of making MHC-binding predictions by combining the results from multiple tools. The predictive performance of each individual tool is first ascertained. These performance data are used to derive weights such that the predictions of tools with better accuracy are given greater credence. The combined tool was evaluated using ten-fold cross-validation and was found to signicantly outperform the individual tools when a high specificity threshold is used. It performs comparably well to the best-performing individual tools at lower specificity thresholds. Finally, it also outperforms the combination of the tools resulting from linear discriminant analysis. Conclusion: A heuristic-based method of combining the results of the individual tools better facilitates the scanning of large proteomes for potential epitopes, yielding more actual high-affinity binders while reporting very few false positives.