A scoping review of asthma and machine learning

OBJECTIVE

The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature.

DATA SOURCES

We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020.

STUDY SELECTION

DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented.

RESULTS

A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status).

CONCLUSIONS

The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence).

Supplemental data for this article is available online at http://dx.doi.org/ .

Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy

DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases.

NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction

Prediction of drug-target interactions (DTIs) plays a significant role in drug development and drug discovery. Although this task requires a large investment in terms of time and cost, especially when it is performed experimentally, the results are not necessarily significant. Computational DTI prediction is a shortcut to reduce the risks of experimental methods. In this study, we propose an effective approach of nonnegative matrix tri-factorization, referred to as NMTF-DTI, to predict the interaction scores between drugs and targets. NMTF-DTI utilizes multiple kernels (similarity measures) for drugs and targets and Laplacian regularization to boost the prediction performance. The performance of NMTF-DTI is evaluated via cross-validation and is compared with existing DTI prediction methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR). We evaluate our method on four gold standard datasets, comparing to other state-of-the-art methods. Cross-validation and a separate, manually created dataset are used to set parameters. The results show that NMTF-DTI outperforms other competing methods. Moreover, the results of a case study also confirm the superiority of NMTF-DTI.

Activation of the Anaphase Promoting Complex Reverses Multiple Drug Resistant Cancer in a Canine Model of Multiple Drug Resistant Lymphoma

Simple Summary

Multiple drug resistant cancers develop all too soon in patients who received successful cancer treatment. A lack of treatment options often leaves palliative care as the last resort. We tested whether the insulin sensitizer, metformin, known to have anti-cancer activity, could impact canines with drug resistant lymphoma when added to chemotherapy. All canines in the study expressed protein markers of drug resistance and within weeks of receiving metformin, the markers were decreased. A microarray was performed, and from four canines assessed, a common set of 290 elevated genes were discovered in tumor cells compared to control cells. This cluster was enriched with genes that stall the cell cycle, with a large component representing substrates of the Anaphase Promoting Complex (APC), which degrades proteins. One canine entered partial remission. RNAs from this canine showed that APC substrates were decreased during remission and elevated again during relapse, suggesting that the APC was impaired in drug resistant canines and restored when remission occurred. We validated our results in cell lines using APC inhibitors and activators. We conclude that the APC may be a vital guardian of the genome and could delay the onset of multiple drug resistance when activated.

Abstract

Like humans, canine lymphomas are treated by chemotherapy cocktails and frequently develop multiple drug resistance (MDR). Their shortened clinical timelines and tumor accessibility make canines excellent models to study MDR mechanisms. Insulin-sensitizers have been shown to reduce the incidence of cancer in humans prescribed them, and we previously demonstrated that they also reverse and delay MDR development in vitro. Here, we treated canines with MDR lymphoma with metformin to assess clinical and tumoral responses, including changes in MDR biomarkers, and used mRNA microarrays to determine differential gene expression. Metformin reduced MDR protein markers in all canines in the study. Microarrays performed on mRNAs gathered through longitudinal tumor sampling identified a 290 gene set that was enriched in Anaphase Promoting Complex (APC) substrates and additional mRNAs associated with slowed mitotic progression in MDR samples compared to skin controls. mRNAs from a canine that went into remission showed that APC substrate mRNAs were decreased, indicating that the APC was activated during remission. In vitro validation using canine lymphoma cells selected for resistance to chemotherapeutic drugs confirmed that APC activation restored MDR chemosensitivity, and that APC activity was reduced in MDR cells. This supports the idea that rapidly pushing MDR cells that harbor high loads of chromosome instability through mitosis, by activating the APC, contributes to improved survival and disease-free duration.

Generation of synthetic antibody fragments with optimal complementarity determining region lengths for Notch-1 recognition

Synthetic antibodies have been engineered against a wide variety of antigens with desirable biophysical, biochemical, and pharmacological properties. Here, we describe the generation and characterization of synthetic antigen-binding fragments (Fabs) against Notch-1. Three single-framework synthetic Fab libraries, named S, F, and modified-F, were screened against the recombinant human Notch-1 extracellular domain using phage display. These libraries were built on a modified trastuzumab framework, containing two or four diversified complementarity-determining regions (CDRs) and different CDR diversity designs. In total, 12 Notch-1 Fabs were generated with 10 different CDRH3 lengths. These Fabs possessed a high affinity for Notch-1 (sub-nM to mid-nM K_Dapp values) and exhibited different binding profiles (mono-, bi-or tri-specific) toward Notch/Jagged receptors. Importantly, we showed that screening focused diversity libraries, implementing next-generation sequencing approaches, and fine-tuning the CDR length diversity provided improved binding solutions for Notch-1 recognition. These findings have implications for antibody library design and antibody phage display.

Signaling differences in peripheral blood mononuclear cells of high and low vaccine responders prior to, and following, vaccination in piglets

Individual variability in responses to vaccination can result in vaccinated subjects failing to develop a protective immune response. Vaccine non-responders can remain susceptible to infection and may compromise efforts to achieve herd immunity. Biomarkers of vaccine unresponsiveness could aid vaccine research and development as well as strategically improve vaccine administration programs. We previously vaccinated piglets (n = 117) against a commercial Mycoplasma hyopneumoniae vaccine (RespiSure-One) and observed in low vaccine responder piglets, as defined by serum IgG antibody titers, differential phosphorylation of peptides involved in pro-inflammatory cytokine signaling within peripheral blood mononuclear cells (PBMCs) prior to vaccination, elevated plasma interferon-gamma concentrations, and lower birth weight compared to high vaccine responder piglets. In the current study, we use kinome analysis to investigate signaling events within PBMCs collected from the same high and low vaccine responders at 2 and 6 days post-vaccination. Furthermore, we evaluate the use of inflammatory plasma cytokines, birthweight, and signaling events as biomarkers of vaccine unresponsiveness in a validation cohort of high and low vaccine responders. Differential phosphorylation events (FDR < 0.05) within PBMCs are established between high and low responders at the time of vaccination and at six days post-vaccination. A subset of these phosphorylation events were determined to be consistently differentially phosphorylated (p < 0.05) in the validation cohort of high and low vaccine responders. In contrast, there were no differences in birth weight (p > 0.5) and plasma IFNγ concentrations at the time of vaccination (p > 0.6) between high and low responders within the validation cohort. The results in this study suggest, at least within this study population, phosphorylation biomarkers are more robust predictors of vaccine responsiveness than other physiological markers.

NTD-DR: Nonnegative tensor decomposition for drug repositioning

Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and target-disease networks, NTD-DR uses these pairwise associations to construct a three-dimensional tensor representing drug-target-disease triplet associations and integrates them with similarity information of drugs, targets, and disease to make a prediction. We compare NTD-DR with recent state-of-the-art methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR) and find that our method outperforms competing methods. Moreover, case studies with five diseases also confirm the reliability of predictions made by NTD-DR. Our proposed method identifies more known associations among the top 50 predictions than other methods. In addition, novel associations identified by NTD-DR are validated by literature analyses.

PIIKA 2.5: Enhanced quality control of peptide microarrays for kinome analysis

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.

Bioinformatics tools used for whole-genome sequencing analysis of Neisseria gonorrhoeae: a literature review

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.

Computational methods for predicting 3D genomic organization from high-resolution chromosome conformation capture data

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.

MDIPA: a microRNA-drug interaction prediction approach based on non-negative matrix factorization

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 KH domain facilitates the substrate specificity and unwinding processivity of DDX43 helicase

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.

Predictive analysis methods for human microbiome data with application to Parkinson's disease

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.

A β-lactamase-producing plasmid from Neisseria gonorrhoeae carrying a unique 6 bp deletion in blaTEM-1 encoding a truncated 24 kDa TEM-1 penicillinase that hydrolyses ampicillin slowly

BACKGROUND

Seven 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.

OBJECTIVES

To compare the DNA sequence of pJRD20 with that of pJD5 and pJD4 (Asia-type) and their TEM-1 β-lactamases.

METHODS

N. 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.

RESULTS

N. 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.

CONCLUSIONS

This 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.

From Beef to Bees: High-Throughput Kinome Analysis to Understand Host Responses of Livestock Species to Infectious Diseases and Industry-Associated Stress

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.

Kinome Analysis of Honeybee (Apis mellifera L.) Dark-Eyed Pupae Identifies Biomarkers and Mechanisms of Tolerance to Varroa Mite Infestation

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.

Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection

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.

Next-generation sequencing-guided identification and reconstruction of antibody CDR combinations from phage selection outputs

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 V_H or V_L. 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.

Genes responsive to rapamycin and serum deprivation are clustered on chromosomes and undergo reorganization within local chromatin environments

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.

Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer

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.

GrapHi-C: graph-based visualization of Hi-C datasets

Objectives

Hi-C is a proximity-based ligation reaction used to detect regions of the genome that are close in 3D space (or “interacting”). Typically, results from Hi-C experiments (contact maps) are visualized as heatmaps or Circos plots. While informative, these visualizations do not directly represent genomic structure and folding, making the interpretation of the underlying 3D genomic organization obscured. Our objective was to generate a graph-based contact map representation that leads to a more intuitive structural visualization.

Results

Normalized contact maps were converted into undirected graphs where each vertex represented a genomic region and each edge represented a detected (intra- and inter-chromosomal) or known (linear) interaction between two regions. Each edge was weighted by the inverse of the linear distance (Hi-C experimental resolution) or the interaction frequency from the contact map. Graphs were generated based on this representation scheme for contact maps from existing fission yeast datasets. Originally, these datasets were used to (1) identify specific principles influencing fission yeast genome organization and (2) uncover changes in fission yeast genome organization during the cell cycle. When compared to the equivalent heatmaps and/or Circos plots, the graph-based visualizations more intuitively depicted the changes in genome organization described in the original studies.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3507-2) contains supplementary material, which is available to authorized users.

EPHB6 augments both development and drug sensitivity of triple-negative breast cancer tumours

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.

Rapamycin reduces fibroblast proliferation without causing quiescence and induces STAT5A/B-mediated cytokine production

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.

DAPPLE 2: a Tool for the Homology-Based Prediction of Post-Translational Modification Sites

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 .

Computational Analysis of the Predicted Evolutionary Conservation of Human Phosphorylation Sites

Protein kinase-mediated phosphorylation is among the most important post-translational modifications. However, few phosphorylation sites have been experimentally identified for most species, making it difficult to determine the degree to which phosphorylation sites are conserved. The goal of this study was to use computational methods to characterize the conservation of human phosphorylation sites in a wide variety of eukaryotes. Using experimentally-determined human sites as input, homologous phosphorylation sites were predicted in all 432 eukaryotes for which complete proteomes were available. For each pair of species, we calculated phosphorylation site conservation as the number of phosphorylation sites found in both species divided by the number found in at least one of the two species. A clustering of the species based on this conservation measure was concordant with phylogenies based on traditional genomic measures. For a subset of the 432 species, phosphorylation site conservation was compared to conservation of both protein kinases and proteins in general. Protein kinases exhibited the highest degree of conservation, while general proteins were less conserved and phosphorylation sites were least conserved. Although preliminary, these data tentatively suggest that variation in phosphorylation sites may play a larger role in explaining phenotypic differences among organisms than differences in the complements of protein kinases or general proteins.

Concordance between RNA-sequencing data and DNA microarray data in transcriptome analysis of proliferative and quiescent fibroblasts

DNA microarrays and RNA sequencing (RNA-seq) are major technologies for performing high-throughput analysis of transcript abundance. Recently, concerns have been raised regarding the concordance of data derived from the two techniques. Using cDNA libraries derived from normal human foreskin fibroblasts, we measured changes in transcript abundance as cells transitioned from proliferative growth to quiescence using both DNA microarrays and RNA-seq. The internal reproducibility of the RNA-seq data was greater than that of the microarray data. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarray values were moderate. The two technologies had good agreement when considering probes with the largest (both positive and negative) fold change (FC) values. An independent technique, quantitative reverse-transcription PCR (qRT-PCR), was used to measure the FC of 76 genes between proliferative and quiescent samples, and a higher correlation was observed between the qRT-PCR data and the RNA-seq data than between the qRT-PCR data and the microarray data.

Induction of tissue- and stressor-specific kinomic responses in chickens exposed to hot and cold stresses

Defining cellular responses at the level of global cellular kinase (kinome) activity is a powerful approach to deciphering complex biology and identifying biomarkers. Here we report on the development of a chicken-specific peptide array and its application to characterizing kinome responses within the breast (pectoralis major) and thigh (iliotibialis) muscles of poultry subject to temperature stress to mimic conditions experienced by birds during commercial transport. Breast and thigh muscles exhibited unique kinome profiles, highlighting the distinct nature of these tissues. Against these distinct backgrounds, tissue- and temperature-specific kinome responses were observed. In breast, both cold and hot stresses activated calcium-dependent metabolic adaptations. Also within breast, but specific to cold stress, was the activation of ErbB signaling as well as dynamic patterns of phosphorylation of AMPK, a key regulatory enzyme of metabolism. In thigh, cold stress induced responses suggestive of the occurrence of tissue damage, including activation of innate immune signaling pathways and tissue repair pathways (TGF-β). In contrast, heat stress in thigh activated pathways associated with protein and fat metabolism through adipocytokine and mammalian target of rapamycin (mTOR) signaling. Defining the responses of these tissues to these stresses through conventional markers of pH, glycolytic potential, and meat quality offered a similar conclusion of the tissue- and stressor-specific responses, validating the kinome results. Collectively, the results of this study highlight the unique cellular responses of breast and thigh tissues to heat and cold stresses and may offer insight into the unique susceptibilities, as well as functional consequences, of these tissues to thermal stress.

Characterization of the host response to pichinde virus infection in the Syrian golden hamster by species-specific kinome analysis

The Syrian golden hamster has been increasingly used to study viral hemorrhagic fever (VHF) pathogenesis and countermeasure efficacy. As VHFs are a global health concern, well-characterized animal models are essential for both the development of therapeutics and vaccines as well as for increasing our understanding of the molecular events that underlie viral pathogenesis. However, the paucity of reagents or platforms that are available for studying hamsters at a molecular level limits the ability to extract biological information from this important animal model. As such, there is a need to develop platforms/technologies for characterizing host responses of hamsters at a molecular level. To this end, we developed hamster-specific kinome peptide arrays to characterize the molecular host response of the Syrian golden hamster. After validating the functionality of the arrays using immune agonists of defined signaling mechanisms (lipopolysaccharide (LPS) and tumor necrosis factor (TNF)-α), we characterized the host response in a hamster model of VHF based on Pichinde virus (PICV(1)) infection by performing temporal kinome analysis of lung tissue. Our analysis revealed key roles for vascular endothelial growth factor (VEGF), interleukin (IL) responses, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling, and Toll-like receptor (TLR) signaling in the response to PICV infection. These findings were validated through phosphorylation-specific Western blot analysis. Overall, we have demonstrated that hamster-specific kinome arrays are a robust tool for characterizing the species-specific molecular host response in a VHF model. Further, our results provide key insights into the hamster host response to PICV infection and will inform future studies with high-consequence VHF pathogens.

Case study: using sequence homology to identify putative phosphorylation sites in an evolutionarily distant species (honeybee)

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.

Bacterial peptides are intensively present throughout the human proteome

Forty bacterial proteomes-20 pathogens and 20 non-pathogens-were examined for amino acid sequence similarity to the human proteome. All bacterial proteomes, independent of their pathogenicity, share hundreds of nonamer sequences with the human proteome. This overlap is very widespread, with one third of human proteins sharing at least one nonapeptide with one of these bacteria. On the whole, the bacteria-versus-human nonamer overlap is numerically defined by 47,610 total perfect matches disseminated through 10,701 human proteins. These findings open new perspectives on the immune relationship between bacteria and host, and might help our understanding of fundamental phenomena such as self-nonself discrimination and tolerance versus auto-reactivity.

No human protein is exempt from bacterial motifs, not even one

The hypothesis that mimicry between a self and a microbial peptide antigen is strictly related to autoimmune pathology remains a debated concept in autoimmunity research. Clear evidence for a causal link between molecular mimicry and autoimmunity is still lacking. In recent studies we have demonstrated that viruses and bacteria share amino acid sequences with the human proteome at such a high extent that the molecular mimicry hypothesis becomes questionable as a causal factor in autoimmunity. Expanding upon our analysis, here we detail the bacterial peptide overlapping to the human proteome at the penta-, hexa-, hepta- and octapeptide levels by exact peptide matching analysis and demonstrate that there does not exist a single human protein that does not harbor a bacterial pentapeptide or hexapeptide motif. This finding suggests that molecular mimicry between a self and a microbial peptide antigen cannot be assumed as a basis for autoimmune pathologies. Moreover, the data are discussed in relation to the microbial immune escape phenomenon and the possible vaccine-related autoimmune effects.

Peptide Arrays for Kinome Analysis of Livestock Species

Reversible protein phosphorylation is a central mechanism for both the transfer of intracellular information and the initiation of cellular responses. Within human medicine, considerable emphasis is placed on understanding and controlling the enzymes (kinases) that are responsible for catalyzing these modifications. This is evident in the prominent use of kinase inhibitors as drugs as well as the trend to understand complex biology and identify biomarkers via characterizations of global kinase (kinome) activity. Despite the demonstrated value of focusing on kinome activity, the application of this perspective to livestock has been restricted by the absence of appropriate research tools. In this review, we discuss the development of software platforms that facilitate the development and application of species-specific peptide arrays for kinome analysis of livestock. Examples of the application of kinomic approaches to a number of priority species (cattle, pigs, and chickens) in a number of biological contexts (infections, biomarker discovery, and food quality) are presented as are emerging trends for kinome analysis of livestock.

A better sequence-read simulator program for metagenomics

Background

There are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors' original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data.

Results

We present BEAR (Better Emulation for Artificial Reads), a program that uses a machine-learning approach to generate reads with lengths and quality values that closely match empirically-derived distributions. BEAR can emulate reads from various sequencing platforms, including Illumina, 454, and Ion Torrent. BEAR requires minimal user input, as it automatically determines appropriate parameter settings from user-supplied data. BEAR also uses a unique method for deriving run-specific error rates, and extracts useful statistics from the metagenomic data itself, such as quality-error models. Many existing simulators are specific to a particular sequencing technology; however, BEAR is not restricted in this way. Because of its flexibility, BEAR is particularly useful for emulating the behaviour of technologies like Ion Torrent, for which no dedicated sequencing simulators are currently available. BEAR is also the first metagenomic sequencing simulator program that automates the process of generating abundances, which can be an arduous task.

Conclusions

BEAR is useful for evaluating data processing tools in genomics. It has many advantages over existing comparable software, such as generating more realistic reads and being independent of sequencing technology, and has features particularly useful for metagenomics work.

Equivalent input produces different output in the UniFrac significance test

Background

UniFrac is a well-known tool for comparing microbial communities and assessing statistically significant differences between communities. In this paper we identify a discrepancy in the UniFrac methodology that causes semantically equivalent inputs to produce different outputs in tests of statistical significance.

Results

The phylogenetic trees that are input into UniFrac may or may not contain abundance counts. An isomorphic transform can be defined that will convert trees between these two formats without altering the semantic meaning of the trees. UniFrac produces different outputs for these equivalent forms of the same input tree. This is illustrated using metagenomics data from a lake sediment study.

Conclusions

Results from the UniFrac tool can vary greatly for the same input depending on the arbitrary choice of input format. Practitioners should be aware of this issue and use the tool with caution to ensure consistency and validity in their analyses. We provide a script to transform inputs between equivalent formats to help researchers achieve this consistency.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-278) contains supplementary material, which is available to authorized users.

Identification of developmentally-specific kinotypes and mechanisms of Varroa mite resistance through whole-organism, kinome analysis of honeybee

Recent investigations associate Varroa destructor (Mesostigmata: Varroidae) parasitism and its associated pathogens and agricultural pesticides with negative effects on colony health, resulting in sporadic global declines in domestic honeybee (Apis mellifera) populations. These events have motivated efforts to develop research tools that can offer insight into the causes of declining bee health as well as identify biomarkers to guide breeding programs. Here we report the development of a bee-specific peptide array for characterizing global cellular kinase activity in whole bee extracts. The arrays reveal distinct, developmentally-specific signaling profiles between bees with differential susceptibility to infestation by Varroa mites. Gene ontology analysis of the differentially phosphorylated peptides indicates that the differential susceptibility to Varroa mite infestation does not reflect compromised immunity; rather, there is evidence for mite-mediated immune suppression within the susceptible phenotype that may reduce the ability of these bees to counter secondary viral infections. This hypothesis is supported by the demonstration of more diverse viral infections in mite-infested, susceptible adult bees. The bee-specific peptide arrays are an effective tool for understanding the molecular basis of this complex phenotype as well as for the discovery and utilization of phosphorylation biomarkers for breeding programs.

Kinotypes: stable species- and individual-specific profiles of cellular kinase activity

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.

PIIKA 2: an expanded, web-based platform for analysis of kinome microarray data

Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA), a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.

DAPPLE: a pipeline for the homology-based prediction of phosphorylation sites

SUMMARY

While many experimentally characterized phosphorylation sites exist for certain organisms, such as human, rat and mouse, few sites are known for other organisms, hampering related research efforts. We have developed a software pipeline called DAPPLE that automates the process of using known phosphorylation sites from other organisms to identify putative sites in an organism of interest.

AVAILABILITY

DAPPLE is available as a web server at http://saphire.usask.ca.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational phosphorylation site prediction in plants using random forests and organism-specific instance weights

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.

Induction of ligand-specific PrP (C) signaling in human neuronal cells

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.

Systems kinomics demonstrates Congo Basin monkeypox virus infection selectively modulates host cell signaling responses as compared to West African monkeypox virus

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.

Comparing the similarity of different groups of bacteria to the human proteome

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.

A systematic approach for analysis of peptide array kinome data

The central roles of kinases in cellular processes and diseases make them highly attractive as indicators of biological responses and as therapeutic targets. Peptide arrays are emerging as an important means of characterizing kinome activity. Currently, the computational tools used to perform high-throughput kinome analyses are not specifically tailored to the nature of the data, which hinders extraction of biological information and overall progress in the field. We have developed a method for kinome analysis, which is implemented as a software pipeline in the R environment. Components and parameters were chosen to address the technical and biological characteristics of kinome microarrays. We performed comparative analysis of kinome data sets that corresponded to stimulation of immune cells with ligands of well-defined signaling pathways: bovine monocytes treated with interferon-γ (IFN-γ), CpG-containing nucleotides, or lipopolysaccharide (LPS). The data sets for each of the treatments were analyzed with our methodology as well as with three other commonly used approaches. The methods were evaluated on the basis of statistical confidence of calculated values with respect to technical and biological variability, and the statistical confidence (P values) by which the known signaling pathways could be independently identified by the pathway analysis of InnateDB (a Web-based resource for innate immunity interactions and pathways). By considering the particular attributes of kinome data, we found that our approach identified more of the peptides involved in the pathways than did the other compared methods and that it did so at a much higher degree of statistical confidence.

Analysis and comparison of the pan-genomic properties of sixteen well-characterized bacterial genera

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.

The oligodeoxynucleotide sequences corresponding to never-expressed peptide motifs are mainly located in the non-coding strand

Background

We study the usage of specific peptide platforms in protein composition. Using the pentapeptide as a unit of length, we find that in the universal proteome many pentapeptides are heavily repeated (even thousands of times), whereas some are quite rare, and a small number do not appear at all. To understand the physico-chemical-biological basis underlying peptide usage at the proteomic level, in this study we analyse the energetic costs for the synthesis of rare and never-expressed versus frequent pentapeptides. In addition, we explore residue bulkiness, hydrophobicity, and codon number as factors able to modulate specific peptide frequencies. Then, the possible influence of amino acid composition is investigated in zero- and high-frequency pentapeptide sets by analysing the frequencies of the corresponding inverse-sequence pentapeptides. As a final step, we analyse the pentadecamer oligodeoxynucleotide sequences corresponding to the never-expressed pentapeptides.

Results

We find that only DNA context-dependent constraints (such as oligodeoxynucleotide sequence location in the minus strand, introns, pseudogenes, frameshifts, etc.) provide a coherent mechanistic platform to explain the occurrence of never-expressed versus frequent pentapeptides in the protein world.

Conclusions

This study is of importance in cell biology. Indeed, the rarity (or lack of expression) of specific 5-mer peptide modules implies the rarity (or lack of expression) of the corresponding n-mer peptide sequences (with n < 5), so possibly modulating protein compositional trends. Moreover the data might further our understanding of the role exerted by rare pentapeptide modules as critical biological effectors in protein-protein interactions.