Peptide immunoarrays for rationale development of vaccines with enhanced cross-reactivity.

Vaccines of enhanced range of protection could help to control newly emerging infectious diseases while providing greater resilience to any subsequent variants. Such "universal vaccines" are an idealized, but unrealized, goal that may benefit from unbiased, high-throughput approaches that define antibody cross-reactivity to enable rational selection of cross-protective epitopes. The priority of this investigation is to establish a pipeline for the identification and preliminary characterization of epitopes with enhanced cross-reactivity.

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.

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

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.

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.

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.

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.

High-resolution analysis of long-term serum antibodies in humans following convalescence of SARS-CoV-2 infection.

Long-term antibody responses to SARS-CoV-2 have focused on responses to full-length spike protein, specific domains within spike, or nucleoprotein. In this study, we used high-density peptide microarrays representing the complete proteome of SARS-CoV-2 to identify binding sites (epitopes) targeted by antibodies present in the blood of COVID-19 resolved cases at 5 months post-diagnosis. Compared to previous studies that evaluated epitope-specific responses early post-diagnosis (< 60 days), we found that epitope-specific responses to nucleoprotein and spike protein have contracted, and that responses to membrane protein have expanded.

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.

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.

Silver: Forging almost Gold Standard Datasets.

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior.

Higher concentrations of vitamin D in Canadian children with juvenile idiopathic arthritis compared to healthy controls are associated with more frequent use of vitamin D supplements and season of birth.

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.

EPIphany-A Platform for Analysis and Visualization of Peptide Immunoarray Data.

Antibodies are critical effector molecules of the humoral immune system. Upon infection or vaccination, populations of antibodies are generated which bind to various regions of the invading pathogen or exogenous agent. Defining the reactivity and breadth of this antibody response provides an understanding of the antigenic determinants and enables the rational development and assessment of vaccine candidates.

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).

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.

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.

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.

Gene Set Analysis: Challenges, Opportunities, and Future Research.

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses.

Kinome profiling of peripheral blood mononuclear cells collected prior to vaccination reveals biomarkers and potential mechanisms of vaccine unresponsiveness in pigs.

Inter-individual variance in host immune responses following vaccination can result in failure to develop protective immunity leaving individuals at risk for infection in addition to compromising herd immunity. While developing more efficacious vaccines is one strategy to mitigate this problem, predicting vaccine responsiveness prior to vaccination could inform which individuals require adjunct disease management strategies. To identify biomarkers of vaccine responsiveness, a cohort of pigs (n = 120) were vaccinated and pigs representing the high (n = 6; 90th percentile) and low (n = 6; 10th percentile) responders based on vaccine-specific antibody responses following vaccination were further analyzed.

Genomic Analysis Reveals Antibiotic-Susceptible Clones and Emerging Resistance in Neisseria gonorrhoeae in Saskatchewan, Canada.

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 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 outbreak and the clonal spread of susceptible isolates in men.

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.

Gen2EpiGUI: User-Friendly Pipeline for Analyzing Whole-Genome Sequencing Data for Epidemiological Studies of Neisseria gonorrhoeae.

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.

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.

Associations of clinical and inflammatory biomarker clusters with juvenile idiopathic arthritis categories.

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.