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A number of studies have demonstrated that patients with autoimmune disease have lower levels of vitamin D prompting speculation that vitamin D might suppress inflammation and immune responses in children with juvenile idiopathic arthritis (JIA). The objective of this study was to compare vitamin D levels in children with JIA at disease onset with healthy children. We hypothesized that children and adolescents with JIA have lower vitamin D levels than healthy children and adolescents. Data from a Canadian cohort of children with new-onset JIA (n= 164, data collection 2007-2012) were compared to Canadian Health Measures Survey (CHMS) data (n=4027, data collection 2007-2011). We compared 25-hydroxy vitamin D (25(OH)D) concentrations with measures of inflammation, vitamin D supplement use, milk intake, and season of birth. Mean 25(OH)D level was significantly higher in patients with JIA (79 ± 3.1 nmol/L) than in healthy controls (68 ± 1.8 nmol/L P.05). Patients with JIA more often used vitamin D containing supplements (50% vs. 7%; P.05). The prevalence of 25(OH)D deficiency (30 nmol/L) was 6% for both groups. Children with JIA with 25(OH)D deficiency or insufficiency (50 nmol/L) had higher C-reactive protein levels. Children with JIA were more often born in the fall and winter compared to healthy children. In contrast to earlier studies, we found vitamin D levels in Canadian children with JIA were higher compared to healthy children and associated with more frequent use of vitamin D supplements. Among children with JIA, low vitamin D levels were associated with indicators of greater inflammation.

Motivation Evidence has shown that microRNAs, one type of small biomolecule, regulate the expression level of genes and play an important role in the development or treatment of diseases. Drugs, as important chemical compounds, can interact with microRNAs and change their functions. The experimental identification of microRNA–drug interactions is time-consuming and expensive. Therefore, it is appealing to develop effective computational approaches for predicting microRNA–drug interactions. Results In this study, a matrix factorization-based method, called the microRNA–drug interaction prediction approach (MDIPA), is proposed for predicting unknown interactions among microRNAs and drugs. Specifically, MDIPA utilizes experimentally validated interactions between drugs and microRNAs, drug similarity and microRNA similarity to predict undiscovered interactions. A path-based microRNA similarity matrix is constructed, while the structural information of drugs is used to establish a drug similarity matrix. To evaluate its performance, our MDIPA is compared with four state-of-the-art prediction methods with an independent dataset and cross-validation. The results of both evaluation methods confirm the superior performance of MDIPA over other methods. Finally, the results of molecular docking in a case study with breast cancer confirm the efficacy of our approach. In conclusion, MDIPA can be effective in predicting potential microRNA–drug interactions. Availability and implementation All code and data are freely available from https://github.com/AliJam82/MDIPA. Supplementary information Supplementary data are available at Bioinformatics online.

The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure–function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.

We have developed a graphical user interface for our Gen2Epi computational pipeline named Gen2EpiGUI. A total of 594 published whole-genome sequence datasets of Neisseria gonorrhoeae were used to validate the program. Gen2Epi facilitates an understandable analysis of N. gonorrhoeae whole-genome sequence data for users with limited bioinformatics skills.

Objective To identify discrete clusters comprising clinical features and inflammatory biomarkers in children with JIA and to determine cluster alignment with JIA categories. Methods A Canadian prospective inception cohort comprising 150 children with JIA was evaluated at baseline (visit 1) and after six months (visit 2). Data included clinical manifestations and inflammation-related biomarkers. Probabilistic principal component analysis identified sets of composite variables, or principal components, from 191 original variables. To discern new clinical-biomarker clusters (clusters), Gaussian mixture models were fit to the data. Newly-defined clusters and JIA categories were compared. Agreement between the two was assessed using Kruskal–Wallis analyses and contingency plots. Results Three principal components recovered 35% (three clusters) and 40% (five clusters) of the variance in patient profiles in visits 1 and 2, respectively. None of the clusters aligned precisely with any of the seven JIA categories but rather spanned multiple categories. Results demonstrated that the newly defined clinical-biomarker lustres are more homogeneous than JIA categories. Conclusion Applying unsupervised data mining to clinical and inflammatory biomarker data discerns discrete clusters that intersect multiple JIA categories. Results suggest that certain groups of patients within different JIA categories are more aligned pathobiologically than their separate clinical categorizations suggest. Applying data mining analyses to complex datasets can generate insights into JIA pathogenesis and could contribute to biologically based refinements in JIA classification.

BackgroundSeven structurally related β-lactamase-producing plasmids have been characterized in penicillinase-producing Neisseria gonorrhoeae (PPNG) isolates. We characterized a variant (i.e. pJRD20, Canada type) of the Africa-type (pJD5) plasmid isolated from N. gonorrhoeae strain 8903.ObjectivesTo compare the DNA sequence of pJRD20 with that of pJD5 and pJD4 (Asia-type) and their TEM-1 β-lactamases.MethodsN. gonorrhoeae 8903 was identified as part of the Gonococcal Antimicrobial Surveillance Program in Canada. β-Lactamase production was assessed using nitrocefin. MICs were determined by agar dilution and Etest methods (CLSI). The DNA sequences of pJRD20, pJD5 and pJD4 were assembled and annotated. The structure of TEM-1 and its penicillin-binding properties were determined by in silico molecular modelling and docking. TEM-1 proteins were characterized by western blot, mass spectrometry and ampicillin hydrolysis assays.ResultsN. gonorrhoeae 8903 exhibited intermediate susceptibility to penicillin with slow β-lactamase activity (i.e. 35 min to hydrolyse nitrocefin). Except for a novel 6 bp deletion starting at the G of the ATG start codon of blaTEM-1, the DNA sequence of pJRD20 was identical to that of pJD5. The TEM-1 β-lactamase produced by pJRD20 is 24 kDa and hydrolyses ampicillin only after several hours.ConclusionsThis unusual PPNG isolate might have been characterized as a non-PPNG owing to its low MIC of penicillin and its very slow hydrolysis of nitrocefin. Given the unusual nature of its TEM-1 β-lactamase, laboratories might consider extending the duration of nitrocefin hydrolysis assays.

Background Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. Methods We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. Results Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. Conclusions This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. Electronic supplementary material The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.

Whole-genome sequencing (WGS) data are well established for the investigation of gonococcal transmission, antimicrobial resistance prediction, population structure determination and population dynamics. A variety of bioinformatics tools, repositories, services and platforms have been applied to manage and analyze Neisseria gonorrhoeae WGS datasets. This review provides an overview of the various bioinformatics approaches and resources used in 105 published studies (as of 30 April 2021). The challenges in the analysis of N. gonorrhoeae WGS datasets, as well as future bioinformatics requirements, are also discussed.

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.

The K-homology (KH) domain is a nucleic acid–binding domain present in many proteins. Recently, we found that the DEAD-box helicase DDX43 contains a KH domain in its N-terminus; however, its function remains unknown. Here, we purified recombinant DDX43 KH domain protein and found that it prefers binding ssDNA and ssRNA. Electrophoretic mobility shift assay and NMR revealed that the KH domain favors pyrimidines over purines. Mutational analysis showed that the GXXG loop in the KH domain is involved in pyrimidine binding. Moreover, we found that an alanine residue adjacent to the GXXG loop is critical for binding. Systematic evolution of ligands by exponential enrichment, chromatin immunoprecipitation–seq, and cross-linking immunoprecipitation–seq showed that the KH domain binds C-/T-rich DNA and U-rich RNA. Bioinformatics analysis suggested that the KH domain prefers to bind promoters. Using 15N-heteronuclear single quantum coherence NMR, the optimal binding sequence was identified as TTGT. Finally, we found that the full-length DDX43 helicase prefers DNA or RNA substrates with TTGT or UUGU single-stranded tails and that the KH domain is critically important for sequence specificity and unwinding processivity. Collectively, our results demonstrated that the KH domain facilitates the substrate specificity and processivity of the DDX43 helicase.

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.

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.

A real-time PCR (RT-PCR) assay was designed for the simultaneous identification of Neisseria gonorrhoeae and its ciprofloxacin susceptibility status. A SYBR green-based multiplex RT-PCR format was used; it comprised two different forward primers and a common reverse primer to detect single nucleotide polymorphisms (SNPs) in gyrA of N. gonorrhoeae . The primer pairs were evaluated for their sensitivity and specificity using genomic DNA from 254 N. gonorrhoeae isolates (82 were ciprofloxacin susceptible and 172 were ciprofloxacin resistant) and 23 non- N. gonorrhoeae species isolates. The performance of the primers was validated using genomic DNA from 100 different N. gonorrhoeae isolates (46 were ciprofloxacin susceptible and 54 were ciprofloxacin resistant) and 52 non- N. gonorrhoeae isolates. The latter panel was revalidated by testing 99 (46 isolates were ciprofloxacin susceptible and 53 isolates were ciprofloxacin resistant) of the N. gonorrhoeae isolates and 23 non- N. gonorrhoeae isolates. These primers detected N. gonorrhoeae and its ciprofloxacin susceptibility status with over 99% sensitivity and specificity for all panels tested. This assay has the potential to be an inexpensive and rapid test for the simultaneous identification of N. gonorrhoeae and its ciprofloxacin susceptibility status.

There is increasing appreciation among researchers and clinicians of the value of investigating biology and pathobiology at the level of cellular kinase (kinome) activity. Kinome analysis provides valuable opportunity to gain insights into complex biology (including disease pathology), identify biomarkers of critical phenotypes (including disease prognosis and evaluation of therapeutic efficacy), and identify targets for therapeutic intervention through kinase inhibitors. The growing interest in kinome analysis has fueled efforts to develop and optimize technologies that enable characterization of phosphorylation-mediated signaling events in a cost-effective, high-throughput manner. In this review, we highlight recent advances to the central technologies currently available for kinome profiling and offer our perspectives on the key challenges remaining to be addressed.

Objective To identify early predictors of disease activity at 18 months in JIA using clinical and biomarker profiling. Methods Clinical and biomarker data were collected at JIA diagnosis in a prospective longitudinal inception cohort of 82 children with non-systemic JIA, and their ability to predict an active joint count of 0, a physician global assessment of disease activity of ≤1 cm, and inactive disease by Wallace 2004 criteria 18 months later was assessed. Correlation-based feature selection and ReliefF were used to shortlist predictors and random forest models were trained to predict outcomes. Results From the original 112 features, 13 effectively predicted 18-month outcomes. They included age, number of active/effused joints, wrist, ankle and/or knee involvement, ESR, ANA positivity and plasma levels of five inflammatory biomarkers (IL-10, IL-17, IL-12p70, soluble low-density lipoprotein receptor-related protein 1 and vitamin D), at enrolment. The clinical plus biomarker panel predicted active joint count = 0, physician global assessment ≤ 1, and inactive disease after 18 months with 0.79, 0.80 and 0.83 accuracy and 0.84, 0.83, 0.88 area under the curve, respectively. Using clinical features alone resulted in 0.75, 0.72 and 0.80 accuracy, and area under the curve values of 0.81, 0.78 and 0.83, respectively. Conclusion A panel of five plasma biomarkers combined with clinical features at the time of diagnosis more accurately predicted short-term disease activity in JIA than clinical characteristics alone. If validated in external cohorts, such a panel may guide more rationally conceived, biologically based, personalized treatment strategies in early JIA.

Whole-genome sequencing was used to identify mutations in antibiotic resistance-conferring genes to compare susceptibility predictions with MICs and to ascertain strain types in 99 isolates of Neisseria gonorrhoeae . Genotypes associated with susceptibility, as well as MIC creep or emerging resistance, were noted. Phylogenomic analysis revealed three distinctive clades and putative gonococcal transmission linkages involving a tetracycline-resistant N. gonorrhoeae outbreak and the clonal spread of susceptible isolates in men.

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.

We previously demonstrated that genome reorganization, through chromosome territory repositioning, occurs concurrently with significant changes in gene expression in normal primary human fibroblasts treated with the drug rapamycin, or stimulated into quiescence. Although these events occurred concomitantly, it is unclear how specific changes in gene expression relate to reorganization of the genome at higher resolution. We used computational analyses, genome organization assays, and microscopy, to investigate the relationship between chromosome territory positioning and gene expression. We determined that despite relocation of chromosome territories, there was no substantial bias in the proportion of genes changing expression on any one chromosome, including chromosomes 10 and 18. Computational analyses identified that clusters of serum deprivation and rapamycin-responsive genes along the linear extent of chromosomes. Chromosome conformation capture (3C) analysis demonstrated the strengthening or loss of specific long-range chromatin interactions in response to rapamycin and quiescence induction, including a cluster of genes containing Interleukin-8 and several chemokine genes on chromosome 4. We further observed that the LIF gene, which is highly induced upon rapamycin treatment, strengthened interactions with up- and down-stream intergenic regions. Our findings indicate that the repositioning of chromosome territories in response to cell stimuli, this does not reflect gene expression changes occurring within physically clustered groups of genes.

Background Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR’s biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data. Results The proposed method, DNP-AAP (deep neural pursuit – average activation potential), was tested on a Neisseria gonorrhoeae dataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively. Conclusions DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.

Microbiome data consists of operational taxonomic unit (OTU) counts characterized by zero-inflation, over-dispersion, and grouping structure among samples. Currently, statistical testing methods are commonly performed to identify OTUs that are associated with a phenotype. The limitations of statistical testing methods include that the validity of p-values/q-values depend sensitively on the correctness of models and that the statistical significance does not necessarily imply predictivity. Predictive analysis using methods such as LASSO is an alternative approach for identifying associated OTUs and for measuring the predictability of the phenotype variable with OTUs and other covariate variables. We investigate three strategies of performing predictive analysis: (1) LASSO: fitting a LASSO multinomial logistic regression model to all OTU counts with specific transformation; (2) screening+GLM: screening OTUs with q-values returned by fitting a GLMM to each OTU, then fitting a GLM model using a subset of selected OTUs; (3) screening+LASSO: fitting a LASSO to a subset of OTUs selected with GLMM. We have conducted empirical studies using three simulation datasets generated using Dirichlet-multinomial models and a real gut microbiome data related to Parkinson's disease to investigate the performance of the three strategies for predictive analysis. Our simulation studies show that the predictive performance of LASSO with appropriate variable transformation works remarkably well on zero-inflated data. Our results of real data analysis show that Parkinson's disease can be predicted based on selected OTUs after the binary transformation, age, and sex with high accuracy (Error Rate = 0.199, AUC = 0.872, AUPRC = 0.912). These results provide strong evidences of the relationship between Parkinson's disease and the gut microbiome.

Background Recent adva1nces in whole genome sequencing (WGS) based technologies have facilitated multi-step applications for predicting antimicrobial resistance (AMR) and investigating the molecular epidemiology of Neisseria gonorrhoeae. However, generating full scaffolds of N. gonorrhoeae genomes from short reads, and the assignment of molecular epidemiological information (NG-MLST, NG-MAST, and NG-STAR) to multiple assembled samples, is challenging due to required manual tasks such as annotating antimicrobial resistance determinants with standard nomenclature for a large number of genomes. Results We present Gen2Epi, a pipeline that assembles short reads into full scaffolds and automatically assigns molecular epidemiological and AMR information to the assembled genomes. Gen2Epi is a command-line tool integrating third-party software and tailored specifically for N. gonorrhoeae. For its evaluation, the Gen2Epi pipeline successfully assembled the WGS short reads from 1484 N. gonorrhoeae samples into full-length genomes for both chromosomes and plasmids and was able to assign in silico molecular determinant information to each dataset automatically. The assemblies were generated using raw as well as trimmed short reads. The median genome coverage of full-length scaffolds and “N” statistics (N50, NG50, and NGA50) were higher than, or comparable to, previously published results and the scaffolding process improved the quality of the draft genome assemblies. Molecular antimicrobial resistant (AMR) determinants identified by Gen2Epi reproduced information for the 1484 samples as previously reported, including NG-MLST, NG-MAST, and NG-STAR molecular sequence types. Conclusions Gen2Epi can be used to assemble short reads into full-length genomes and assign accurate molecular marker and AMR information automatically from NG-STAR, NG-MAST, and NG-MLST. Gen2Epi is publicly available under “CC BY-NC 2.0 CA” Creative Commons licensing as a VirtualBox image containing the constituent software components running on the LINUX operating system (CentOS 7). The image and associated documentation are available via anonymous FTP at ftp://www.cs.usask.ca/pub/combi or ftp://ftp.cs.usask.ca/pub/combi Electronic supplementary material The online version of this article (10.1186/s12864-019-5542-3) contains supplementary material, which is available to authorized users.

Metformin is a widely-used treatment for type 2 diabetes and is reported to extend health and lifespan as a caloric restriction (CR) mimetic. Although the benefits of metformin are well documented, the impact of this compound on the function and organization of the genome in normal tissues is unclear. To explore this impact, primary human fibroblasts were treated in culture with metformin resulting in a significant decrease in cell proliferation without evidence of cell death. Furthermore, metformin induced repositioning of chromosomes 10 and 18 within the nuclear volume indicating altered genome organization. Transcriptome analyses from RNA sequencing datasets revealed that alteration in growth profiles and chromosome positioning occurred concomitantly with changes in gene expression profiles. We further identified that different concentrations of metformin induced different transcript profiles; however, significant enrichment in the activator protein 1 (AP-1) transcription factor network was common between the different treatments. Comparative analyses revealed that metformin induced divergent changes in the transcriptome than that of rapamycin, another proposed mimetic of CR. Promoter analysis and chromatin immunoprecipitation assays of genes that changed expression in response to metformin revealed enrichment of the transcriptional regulator forkhead box O3a (FOXO3a) in normal human fibroblasts, but not of the predicted serum response factor (SRF). Therefore, we have demonstrated that metformin has significant impacts on genome organization and function in normal human fibroblasts, different from those of rapamycin, with FOXO3a likely playing a role in this response.

Gene set analysis is a quantitative approach for generating biological insight from gene expression datasets. The abundance of gene set analysis methods speaks to their popularity, but raises the question of the extent to which results are affected by the choice of method. Our systematic analysis of 13 popular methods using 6 different datasets, from both DNA microarray and RNA-Seq origin, shows that this choice matters a great deal. We observed that the overall number of gene sets reported by each method differed by up to 2 orders of magnitude, and there was a bias toward reporting large gene sets with some methods. Furthermore, there was substantial disagreement between the 20 most statistically significant gene sets reported by the methods. This was also observed when expanding to the 100 most statistically significant reported gene sets. For different datasets of the same phenotype/condition, the top 20 and top 100 most significant results also showed little to no agreement even when using the same method. GAGE, PAGE, and ORA were the only methods able to achieve relatively high reproducibility when comparing the 20 and 100 most statistically significant gene sets. Biological validation on a juvenile idiopathic arthritis (JIA) dataset showed wide variation in terms of the relevance of the top 20 and top 100 most significant gene sets to known biology of the disease, where GAGE predicted the most relevant gene sets, followed by GSEA, ORA, and PAGE.

Next-generation sequencing (NGS) technologies have been employed in several phage display platforms for analyzing natural and synthetic antibody sequences and for identifying and reconstructing single-chain variable fragments (scFv) and antigen-binding fragments (Fab) not found by conventional ELISA screens. In this work, we developed an NGS-assisted antibody discovery platform by integrating phage-displayed, single-framework, synthetic Fab libraries. Due to limitations in attainable read and amplicon lengths, NGS analysis of Fab libraries and selection outputs is usually restricted to either VH or VL. Since this information alone is not sufficient for high-throughput reconstruction of Fabs, we developed a rapid and simple method for linking and sequencing all diversified CDRs in phage Fab pools. Our method resulted in a reliable and straightforward platform for converting NGS information into Fab clones. We used our NGS-assisted Fab reconstruction method to recover low-frequency rare clones from phage selection outputs. While previous studies chose rare clones for rescue based on their relative frequencies in sequencing outputs, we chose rare clones for reconstruction from less-frequent CDRH3 lengths. In some cases, reconstructed rare clones (frequency ∼0.1%) showed higher affinity and better specificity than high-frequency top clones identified by Sanger sequencing, highlighting the significance of NGS-based approaches in synthetic antibody discovery.

Mycobacterium avium subsp. paratuberculosis is the causative agent of Johne's disease (JD) in cattle. M. avium subsp. paratuberculosis infects the gastrointestinal tract of calves, localizing and persisting primarily in the distal ileum. A high percentage of cattle exposed to M. avium subsp. paratuberculosis do not develop JD, but the mechanisms by which they resist infection are not understood. Here, we merge an established in vivo bovine intestinal segment model for M. avium subsp. paratuberculosis infection with bovine-specific peptide kinome arrays as a first step to understanding how infection influences host kinomic responses at the site of infection. Application of peptide arrays to in vivo tissue samples represents a critical and ambitious step in using this technology to understand host-pathogen interactions. Kinome analysis was performed on intestinal samples from 4 ileal segments subdivided into 10 separate compartments (6 M. avium subsp. paratuberculosis -infected compartments and 4 intra-animal controls) using bovine-specific peptide arrays. Kinome data sets clustered into two groups, suggesting unique binary responses to M. avium subsp. paratuberculosis . Similarly, two M. avium subsp. paratuberculosis -specific immune responses, characterized by different antibody, T cell proliferation, and gamma interferon (IFN-γ) responses, were also observed. Interestingly, the kinomic groupings segregated with the immune response groupings. Pathway and gene ontology analyses revealed that differences in innate immune and interleukin signaling and particular differences in the Wnt/β-catenin pathway distinguished the kinomic groupings. Collectively, kinome analysis of tissue samples offers insight into the complex cellular responses induced by M. avium subsp. paratuberculosis in the ileum and provides a novel method to understand mechanisms that alter the balance between cell-mediated and antibody responses to M. avium subsp. paratuberculosis infection.

Motivation: Phosphorylation is the most important post-translational modification in eukaryotes. Although many computational phosphorylation site prediction tools exist for mammals, and a few were created specifically for Arabidopsis thaliana, none are currently available for other plants. Results: In this article, we propose a novel random forest-based method called PHOSFER (PHOsphorylation Site FindER) for applying phosphorylation data from other organisms to enhance the accuracy of predictions in a target organism. As a test case, PHOSFER is applied to phosphorylation sites in soybean, and we show that it more accurately predicts soybean sites than both the existing Arabidopsis-specific predictors, and a simpler machine-learning scheme that uses only known phosphorylation sites and non-phosphorylation sites from soybean. In addition to soybean, PHOSFER will be extended to other organisms in the near future. Availability: PHOSFER is available via a web interface at http://saphire.usask.ca. Contact: brett.trost@usask.ca Supplementary information: Supplementary data are available at Bioinformatics online.

The post-translational modification of proteins is critical for regulating their function. Although many post-translational modification sites have been experimentally determined, particularly in certain model organisms, experimental knowledge of these sites is severely lacking for many species. Thus, it is important to be able to predict sites of post-translational modification in such species. Previously, we described DAPPLE, a tool that facilitates the homology-based prediction of one particular post-translational modification, phosphorylation, in an organism of interest using known phosphorylation sites from other organisms. Here, we describe DAPPLE 2, which expands and improves upon DAPPLE in three major ways. First, it predicts sites for many post-translational modifications (20 different types) using data from several sources (15 online databases). Second, it has the ability to make predictions approximately 2-7 times faster than DAPPLE depending on the database size and the organism of interest. Third, it simplifies and accelerates the process of selecting predicted sites of interest by categorizing them based on gene ontology terms, keywords, and signaling pathways. We show that DAPPLE 2 can successfully predict known human post-translational modification sites using, as input, known sites from species that are either closely (e.g., mouse) or distantly (e.g., yeast) related to humans. DAPPLE 2 can be accessed at http://saphire.usask.ca/saphire/dapple2 .

Motivation: Kinase-mediated phosphorylation is the central mechanism of post-translational modification to regulate cellular responses and phenotypes. Signaling defects associated with protein phosphorylation are linked to many diseases, particularly cancer. Characterizing protein kinases and their substrates enhances our ability to understand and treat such diseases and broadens our knowledge of signaling networks in general. While most or all protein kinases have been identified in well-studied eukaryotes, the sites that they phosphorylate have been only partially elucidated. Experimental methods for identifying phosphorylation sites are resource intensive, so the ability to computationally predict potential sites has considerable value. Results: Many computational techniques for phosphorylation site prediction have been proposed, most of which are available on the web. These techniques differ in several ways, including the machine learning technique used; the amount of sequence information used; whether or not structural information is used in addition to sequence information; whether predictions are made for specific kinases or for kinases in general; and sources of training and testing data. This review summarizes, categorizes and compares the available methods for phosphorylation site prediction, and provides an overview of the challenges that are faced when designing predictors and how they have been addressed. It should therefore be useful both for those wishing to choose a phosphorylation site predictor for their particular biological application, and for those attempting to improve upon established techniques in the future. Contact: brett.trost@usask.ca

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.

Alignment of protein sequences is fundamental in analyzing homology, evolutionary events and functional relationships. Searching for the epitopic peptide platform underlying hepatitis C virus (HCV) infection and autoimmune phenomena, we have used sequence-sequence peptide matching to compare the HCV polyprotein sequence to the human proteome. The following results were obtained: (1) pentamers from HCV polyprotein have a widespread and high level of similarity to a large number of human proteins (19,605 human proteins, that is 57.6% of the human proteome); (2) remarkable similarity between the two proteomes persists even using longer peptide motifs as probes for identity scanning; (3) only a limited number of HCV pentameric fragments have no similarity to the human host, so representing molecular sequence signatures of the virus. We conclude that the widespread sharing of numerous perfect exact matches between HCV and human proteomes might explain HCV persistence in humans.

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.

In tandem mass spectrometry (MS/MS), there are several different fragmentation techniques possible, including, collision-induced dissociation (CID) higher energy collisional dissociation (HCD), electron-capture dissociation (ECD), and electron transfer dissociation (ETD). When using pairs of spectra for de novo peptide sequencing, the most popular methods are designed for CID (or HCD) and ECD (or ETD) spectra because of the complementarity between them. Less attention has been paid to the use of CID and HCD spectra pairs. In this study, a new de novo peptide sequencing method is proposed for these spectra pairs. This method includes a CID and HCD spectra merging criterion and a parent mass correction step, along with improvements to our previously proposed algorithm for sequencing merged spectra. Three pairs of spectral datasets were used to investigate and compare the performance of the proposed method with other existing methods designed for single spectrum (HCD or CID) sequencing. Experimental results showed that full-length peptide sequencing accuracy was increased significantly by using spectra pairs in the proposed method, with the highest accuracy reaching 81.31%.

Microarray technology has produced a huge body of time-course gene expression data. Such gene expression data has proved useful in genomic disease diagnosis and genomic drug design. The challenge is how to uncover useful information in such data. Cluster analysis has played an important role in analyzing gene expression data. Many distance/correlation- and static model-based clustering techniques have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterize the data and that should be considered in cluster analysis so as to obtain high quality clustering. This paper proposes a dynamic model-based clustering method for time-course gene expression data. The proposed method regards a time-course gene expression dataset as a set of time series, generated by a number of stochastic processes. Each stochastic process defines a cluster and is described by an autoregressive model. A relocation-iteration algorithm is proposed to identity the model parameters and posterior probabilities are employed to assign each gene to an appropriate cluster. A bootstrapping method and an average adjusted Rand index (AARI) are employed to measure the quality of clustering. Computational experiments are performed on a synthetic and three real time-course gene expression datasets to investigate the proposed method. The results show that our method allows the better quality clustering than other clustering methods (e.g. k-means) for time-course gene expression data, and thus it is a useful and powerful tool for analyzing time-course gene expression data.

Supplemental data for this article can be accessed on the publisher’s website. © 2015 The Author(s) & Taylor & Francis Group, LLC. Rapamycin is a well-known inhibitor of the Target of Rapamycin (TOR) signaling cascade; however, the impact of this drug on global genome function and organization in normal primary cells is poorly understood. To explore this impact, we treated primary human foreskin fibroblasts with rapamycin and observed a decrease in cell proliferation without causing cell death. Upon rapamycin treatment chromosomes 18 and 10 were repositioned to a location similar to that of fibroblasts induced into quiescence by serum reduction. Although similar changes in positioning occurred, comparative transcriptome analyses demonstrated significant divergence in gene expression patterns between rapamycin-treated and quiescence-induced fibroblasts. Rapamycin treatment induced the upregulation of cytokine genes, including those from the Interleukin (IL)-6 signaling network, such as IL-8 and the Leukemia Inhibitory Factor (LIF), while quiescent fibroblasts demonstrated up-regulation of genes involved in the complement and coagulation cascade. In addition, genes significantly up-regulated by rapamycin treatment demonstrated increased promoter occupancy of the transcription factor Signal Transducer and Activator of Transcription 5A/B (STAT5A/B). In summary, we demonstrated that the treatment of fibroblasts with rapamycin decreased proliferation, caused chromosome territory repositioning and induced STAT5A/B-mediated changes in gene expression enriched for cytokines. Brunel Progeria Research Fund (UK), Canadian Institutes of Health Research, NSERC Discovery Grant, NSERC USRA program, Royal Society Research Grant (UK), University of Saskatchewan NSERC President’s Fund, Saskatchewan Innovation and Opportunity Fund.

Ebola virus (EBOV) causes a severe hemorrhagic disease in humans and nonhuman primates, with a median case fatality rate of 78.4%. Although EBOV is considered a public health concern, there is a relative paucity of information regarding the modulation of the functional host response during infection. We employed temporal kinome analysis to investigate the relative early, intermediate, and late host kinome responses to EBOV infection in human hepatocytes. Pathway overrepresentation analysis and functional network analysis of kinome data revealed that transforming growth factor (TGF-β)-mediated signaling responses were temporally modulated in response to EBOV infection. Upregulation of TGF-β signaling in the kinome data sets correlated with the upregulation of TGF-β secretion from EBOV-infected cells. Kinase inhibitors targeting TGF-β signaling, or additional cell receptors and downstream signaling pathway intermediates identified from our kinome analysis, also inhibited EBOV replication. Further, the inhibition of select cell signaling intermediates identified from our kinome analysis provided partial protection in a lethal model of EBOV infection. To gain perspective on the cellular consequence of TGF-β signaling modulation during EBOV infection, we assessed cellular markers associated with upregulation of TGF-β signaling. We observed upregulation of matrix metalloproteinase 9, N-cadherin, and fibronectin expression with concomitant reductions in the expression of E-cadherin and claudin-1, responses that are standard characteristics of an epithelium-to-mesenchyme-like transition. Additionally, we identified phosphorylation events downstream of TGF-β that may contribute to this process. From these observations, we propose a model for a broader role of TGF-β-mediated signaling responses in the pathogenesis of Ebola virus disease. IMPORTANCE Ebola virus (EBOV), formerly Zaire ebolavirus, causes a severe hemorrhagic disease in humans and nonhuman primates and is the most lethal Ebola virus species, with case fatality rates of up to 90%. Although EBOV is considered a worldwide concern, many questions remain regarding EBOV molecular pathogenesis. As it is appreciated that many cellular processes are regulated through kinase-mediated phosphorylation events, we employed temporal kinome analysis to investigate the functional responses of human hepatocytes to EBOV infection. Administration of kinase inhibitors targeting signaling pathway intermediates identified in our kinome analysis inhibited viral replication in vitro and reduced EBOV pathogenesis in vivo . Further analysis of our data also demonstrated that EBOV infection modulated TGF-β-mediated signaling responses and promoted “mesenchyme-like” phenotypic changes. Taken together, these results demonstrated that EBOV infection specifically modulates TGF-β-mediated signaling responses in epithelial cells and may have broader implications in EBOV pathogenesis.

The majority of scientific resources are devoted to studying a relatively small number of model species, meaning that the ability to translate knowledge across species is of considerable importance. Obtaining species-specific knowledge enables targeted investigations of the biology and pathobiology of a particular species, and facilitates comparative analyses. Phosphorylation is the most widespread posttranslational modification in eukaryotes, and although many phosphorylation sites have been experimentally identified for some species, little or no data are available for others. Using the honeybee as a test organism, this case study illustrates the process of using protein sequence homology to identify putative phosphorylation sites in a species of interest using experimentally determined sites from other species. A number of issues associated with this process are examined and discussed. Several databases of experimentally determined phosphorylation sites exist; however, it can be difficult for the nonspecialist to ascertain how their contents compare. Thus, this case study assesses the content and comparability of several phosphorylation site databases. Additional issues examined include the efficacy of homology-based phosphorylation site prediction, the impact of the level of evolutionary relatedness between species in making these predictions, the ability to translate knowledge of phosphorylation sites across large evolutionary distances and the criteria that should be used in selecting probable phosphorylation sites in the species of interest. Although focusing on phosphorylation, the issues discussed here also apply to the homology-based cross-species prediction of other posttranslational modifications, as well as to sequence motifs in general.

Using crude whole-genome assemblies, we analyzed 25 isolates of Neisseria gonorrhoeae by using a high-resolution single nucleotide polymorphism (SNP) approach for nine housekeeping genes, characterizing penA alleles, and antimicrobial susceptibility phenotypes coupled with population structure analysis. Two clonal complexes, characterized by their spatial and geographical persistence, were identified. In addition, the clonal spread of penicillin-resistant/intermediate phenotypes and a novel introduction of the azithromycin resistance phenotype in Saskatchewan, Canada, were ascertained using this method.

Thrombin and hypoxia are important players in breast cancer progression. Breast cancers often develop drug resistance, but mechanisms linking thrombin and hypoxia to drug resistance remain unresolved. Our studies using Doxorubicin (DOX) resistant MCF7 breast cancer cells reveals a mechanism linking DOX exposure with hypoxic induction of DOX resistance. Global expression changes between parental and DOX resistant MCF7 cells were examined. Westerns, Northerns and immunocytochemistry were used to validate drug resistance and differentially expressed genes. A cluster of genes involved in the anticoagulation pathway, with Tissue Factor Pathway Inhibitor 1 (TFPI1) the top hit, was identified. Plasmids overexpressing TFPI1 were utilized, and 1% O2 was used to test the effects of hypoxia on drug resistance. Lastly, microarray datasets from patients with drug resistant breast tumors were interrogated for TFPI1 expression levels. TFPI1 protein levels were found elevated in 3 additional DOX resistant cells lines, from humans and rats, indicating evolutionarily conservation of the effect. Elevated TFPI1 in DOX resistant cells was active, as thrombin protein levels were coincidentally low. We observed elevated HIF1α protein in DOX resistant cells, and in cells with forced expression of TFPI1, suggesting TFPI1 induces HIF1α. TFPI1 also induced c-MYC, c-SRC, and HDAC2 protein, as well as DOX resistance in parental cells. Growth of cells in 1% O2 induced elevated HIF1α, BCRP and MDR-1 protein, and these cells were resistant to DOX. Our in vitro results were consistent with in vivo patient datasets, as tumors harboring increased BCRP and MDR-1 expression also had increased TFPI1 expression. Our observations are clinically relevant indicating that DOX treatment induces an anticoagulation cascade, leading to inhibition of thrombin and the expression of HIF1α. This in turn activates a pathway leading to drug resistance.

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.

Cellular prion protein (PrP (C) ) has attracted considerable attention for its role in transmissible spongiform encephalopathies (TSEs). In spite of being a point of intense research effort critical questions still remain regarding the physiological function of PrP (C) and how these functions may change with the conversion of the protein into the infectious and pathological conformation (PrP (Sc) ). While emerging evidence suggests PrP (C/Sc) are involved in signal transduction there is little consensus on the signaling pathways associated with the normal and diseased states. The purported involvement of PrP (C) in signal transduction, and the association of TSEs with neural pathology, makes kinome analysis of human neurons an interesting and appropriate model to characterize patterns of signal transduction following activation of PrP (C) by two commonly employed experimental ligands; antibody-induced dimerization by 6H4 and the amino acids 106-126 PrP peptide fragment (PrP 106-126). Analysis of the induced kinome responses reveals distinct patterns of signaling activity following each treatment. Specifically, stimulation of human neurons with the 6H4 antibody results in alterations in mitogen activated protein kinase (MAPK) signaling pathways while the 106-126 peptide activates growth factor related signaling pathways including vascular endothelial growth factor (VEGF) signaling and the phosphoinositide-3 kinase (PI3K) pathway. These pathways were validated through independent functional assays. Collectively these results indicate that stimulation of PrP (C) with distinct ligands, even within the same cell type, results in unique patterns of signaling. While this investigation highlights the apparent functional versatility of PrP (C) as a signaling molecule and may offer insight into cellular mechanisms of TSE pathology it also emphasizes the potential dangers associated with attributing activation of specific intracellular events to particular receptors through artificial models of receptor activation.

Numerous aspects of the relationship between bacteria and human have been investigated. One aspect that has recently received attention is sequence overlap at the proteomic level. However, there has not yet been a study that comprehensively characterizes the level of sequence overlap between bacteria and human, especially as it relates to bacterial characteristics like pathogenicity, G-C content, and proteome size. In this study, we began by performing a general characterization of the range of bacteria-human similarity at the proteomic level, and identified characteristics of the most- and least-similar bacterial species. We then examined the relationship between proteomic similarity and numerous other variables. While pathogens and nonpathogens had comparable similarity to the human proteome, pathogens causing chronic infections were found to be more similar to the human proteome than those causing acute infections. Although no general correspondence between a bacterium’s proteome size and its similarity to the human proteome was noted, no bacteria with small proteomes had high similarity to the human proteome. Finally, we discovered an interesting relationship between similarity and a bacterium’s G-C content. While the relationship between bacteria and human has been studied from many angles, their proteomic similarity still needs to be examined in more detail. This paper sheds further light on this relationship, particularly with respect to immunity and pathogenicity.

Monkeypox virus (MPXV) is comprised of two clades: Congo Basin MPXV, with an associated case fatality rate of 10%, and Western African MPXV, which is associated with less severe infection and minimal lethality. We thus postulated that Congo Basin and West African MPXV would differentially modulate host cell responses and, as many host responses are regulated through phosphorylation independent of transcription or translation, we employed systems kinomics with peptide arrays to investigate these functional host responses. Using this approach we have demonstrated that Congo Basin MPXV infection selectively down-regulates host responses as compared with West African MPXV, including growth factor- and apoptosis-related responses. These results were confirmed using fluorescence-activated cell sorting analysis demonstrating that West African MPXV infection resulted in a significant increase in apoptosis in human monocytes as compared with Congo Basin MPXV. Further, differentially phosphorylated kinases were identified through comparison of our MPXV data sets and validated as potential targets for pharmacological inhibition of Congo Basin MPXV infection, including increased Akt S473 phosphorylation and decreased p53 S15 phosphorylation. Inhibition of Akt S473 phosphorylation resulted in a significant decrease in Congo Basin MPXV virus yield (261-fold) but did not affect West African MPXV. In addition, treatment with staurosporine, an apoptosis activator resulted in a 49-fold greater decrease in Congo Basin MPXV yields as compared with West African MPXV. Thus, using a systems kinomics approach, our investigation demonstrates that West African and Congo Basin MPXV differentially modulate host cell responses and has identified potential host targets of therapeutic interest.

The proteomes catalogued in the UniRef100 database were collected into a single proteome set and examined for actual versus theoretical pentapeptide occurrences. We found a highly diversified degree of pentapeptide redundancy. Numerically, 953 pentamers are expressed only once in the protein world, whereas 103 pentamers occur more than 50,000 times. Moreover, it seems that 417 potentially possible pentapeptides are not present in the protein world. On the whole, tracing the redundancy profile of the protein world as a function of pentapeptide occurrences reveals a quasi-Gaussian curve, with tails representing scarcely and repeatedly occurring 5-mers. Analysis of physico-chemical-biological parameters shows that codon number is the main factor influencing and favoring specific pentapeptide frequencies in the universal proteome composition. That is, when compared to the set of never-expressed 5-mers, the pentapeptides frequently represented in the universal proteome are endowed with a higher number of multi-codonic amino acids. In contrast, the bulkiness degree and the hydrophobicity level play a smaller role. Unexpectedly, the heat of formation of pentapeptide appears to have the least influence.

There is some evidence to suggest that peptide segments that are found rarely or never in the host proteome play a role in the immune response to disease-related proteins, both those derived from microbes and those derived from the host itself. We conjecture that this pattern may extend to human proto-oncoproteins. Our hypothesis in this study is that the frequency of rare peptide segments in sets of human proto-oncoproteins is significantly higher than in sets of control proteins, and we show that this is the case. Possible immunological implications of this observation are discussed.

We recently compared the HCV polyprotein to the human proteome in order to test whether amino acid sequences unique to the virus could represent immunodominant epitopic determinants of the human humoral immune response against HCV. We identified a relatively limited number of HCV fragments with no/low similarity to the human host that represented exclusive HCV motifs. In this study, the peptides corresponding to low/zero similarity sequences were synthesized and assayed with HCV-infected sera. With different patterns, the synthetic HCV peptides corresponding to low/zero similarity sequences were found to be immunoreactive. In particular, the HCV E1 (315-323) HRMAWDMMM, HCV E2/NS1 (547-555) NWFGCTWMN, and HCV NS5 (2638-2646) YDTRCFDST sequences were immunodominant in the HCV-infected cohort under study. These three peptides correspond to sequences that are endowed with low-similarity to the human proteome, are highly conserved among various HCV strains, and have, potentially, a scarce susceptibility to proteolytic attacks. These data may be of help in defining the multiple factors which concur in the modulation of the human immune response against HCV, eventually providing information for the design of effective anti-HCV vaccines.