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/ .

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.

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.

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 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.

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 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.

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.

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. 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.

Clinical and associated inflammatory biomarker features predictive of short-term outcomes in non-systemic juvenile idiopathic arthritis.

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.

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. gonorrhoeae whole-genome sequence data for users with limited bioinformatics skills.

Size matters: how sample size affects the reproducibility and specificity of gene set analysis.

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.

Transcriptome Analysis Identifies Plasmodiophora brassicae Secondary Infection Effector Candidates.

Plasmodiophora brassicae (Wor.) is an obligate intracellular plant pathogen affecting Brassicas worldwide. Identification of effector proteins is key to understanding the interaction between P. brassicae and its susceptible host plants. To date, there is very little information available on putative effector proteins secreted by P. brassicae during a secondary infection of susceptible host plants, resulting in root gall production. A bioinformatics pipeline approach to RNA-Seq data from Arabidopsis thaliana (L.) Heynh. root tissues at 17, 20, and 24 d postinoculation (dpi) identified 32 small secreted P. brassicae proteins (SSPbPs) that were highly expressed over this secondary infection time frame. Functional signal peptides were confirmed for 31 of the SSPbPs, supporting the accuracy of the pipeline designed to identify secreted proteins. Expression profiles at 0, 2, 5, 7, 14, 21, and 28 dpi verified the involvement of some of the SSPbPs in secondary infection. For seven of the SSPbPs, a functional domain was identified using Blast2GO and 3D structure analysis and domain functionality was confirmed for SSPbP22, a kinase localized to the cytoplasm and nucleus.

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.

Gen2Epi: an automated whole-genome sequencing pipeline for linking full genomes to antimicrobial susceptibility and molecular epidemiological data in Neisseria gonorrhoeae.

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.

A ß-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 ß-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 ß-lactamases. METHODS: N. gonorrhoeae 8903 was identified as part of the Gonococcal Antimicrobial Surveillance Program in Canada. ß-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 ß-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 ß-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 ß-lactamase, laboratories might consider extending the duration of nitrocefin hydrolysis assays.

Transcriptome analysis of response to Plasmodiophora brassicae infection in the Arabidopsis shoot and root.

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

Multiplex Real-Time PCR Assay for Simultaneous Identification of Neisseria gonorrhoeae and Its Ciprofloxacin Susceptibility Status.

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.

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.

Technological advances for interrogating the human kinome.

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.

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.

De novo peptide sequencing using CID and HCD spectra pairs.

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%.