Gene Expression Profiling in Pediatric Appendicitis.

Importance: Appendicitis is the most common indication for urgent surgery in the pediatric population, presenting across a range of severity and with variable complications. Differentiating simple appendicitis (SA) and perforated appendicitis (PA) on presentation may help direct further diagnostic workup and appropriate therapy selection, including antibiotic choice and timing of surgery. Objective: To provide a mechanistic understanding of the differences in disease severity of appendicitis with the objective of developing improved diagnostics and treatments, specifically for the pediatric population. Design, Setting, and Participants: The Gene Expression Profiling of Pediatric Appendicitis (GEPPA) study was a single-center prospective exploratory diagnostic study with transcriptomic profiling of peripheral blood collected from a cohort of children aged 5 to 17 years with abdominal pain and suspected appendicitis between November 2016 and April 2017 at the Alberta Children's Hospital in Calgary, Alberta, Canada, with data analysis reported in August 2023. There was no patient follow-up in this study. Exposure: SA, PA, or nonappendicitis abdominal pain. Main Outcomes and Measures: Blood transcriptomics was used to develop a hypothesis of underlying mechanistic differences between SA and PA to build mechanistic hypotheses and blood-based diagnostics. Results: Seventy-one children (mean [SD] age, 11.8 [3.0] years; 48 [67.6%] male) presenting to the emergency department with abdominal pain and suspected appendicitis were investigated using whole-blood transcriptomics. A central role for immune system pathways was revealed in PA, including a dampening of major innate interferon responses. Gene expression changes in patients with PA were consistent with downregulation of immune response and inflammation pathways and shared similarities with gene expression signatures derived from patients with sepsis, including the most severe sepsis endotypes. Despite the challenges in identifying early biomarkers of severe appendicitis, a 4-gene signature that was predictive of PA compared to SA, with an accuracy of 85.7% (95% CI, 72.8-94.1) was identified. Conclusions: This study found that PA was complicated by a dysregulated immune response. This finding should inform improved diagnostics of severity, early management strategies, and prevention of further postsurgical complications.

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

A curated collection of human vaccination response signatures.

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard ( www.hipc-dashboard.org ), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.

Gene Expression Profiling: Identification of Novel Pathways and Potential Biomarkers in Severe Acute Pancreatitis.

BACKGROUND: Determining the risk of developing severe acute pancreatitis (AP) on presentation to hospital is difficult but vital to enable early management decisions that reduce morbidity and mortality. The objective of this study was to determine global gene expression profiles of patients with different acute pancreatitis severity to identify genes and molecular mechanisms involved in the pathogenesis of severe AP. STUDY DESIGN: AP patients (n = 87) were recruited within 24 hours of admission to the Emergency Department and were confirmed to exhibit at least 2 of the following features: (1) abdominal pain characteristic of AP, (2) serum amylase and/or lipase more than 3-fold the upper laboratory limit considered normal, and/or (3) radiographically demonstrated AP on CT scan. Severity was defined according to the Revised Atlanta classification. Thirty-two healthy volunteers were also recruited and peripheral venous blood was collected for performing RNA-Seq. RESULTS: In severe AP, 422 genes (185 upregulated, 237 downregulated) were significantly differentially expressed when compared with moderately severe and mild cases. Pathway analysis revealed changes in specific innate and adaptive immune, sepsis-related, and surface modification pathways in severe AP. Data-driven approaches revealed distinct gene expression groups (endotypes), which were not entirely overlapping with the clinical Atlanta classification. Importantly, severe and moderately severe AP patients clustered away from healthy controls, whereas mild AP patients did not exhibit any clear separation, suggesting distinct underlying mechanisms that may influence severity of AP. CONCLUSION: There were significant differences in gene expression affecting the severity of AP, revealing a central role of specific immunological pathways. Despite the existence of patient endotypes, a 4-gene transcriptomic signature (S100A8, S100A9, MMP25, and MT-ND4L) was determined that can predict severe AP with an accuracy of 64%.

PaIntDB: network-based omics integration and visualization using protein-protein interactions in Pseudomonas aeruginosa.

SUMMARY: The Pseudomonas aeruginosa Interaction Database, PaIntDB, is an intuitive web-based tool for network-based systems biology analyses using protein-protein interactions (PPI) in this medically important pathogen. It enables the integration and visualization of omics analyses including RNA-Seq and Tn-Seq. High-throughput datasets can be mapped onto PPI networks, which can be explored visually and filtered to uncover novel putative molecular pathways related to the conditions of study. PaIntDB contains the most comprehensive P.aeruginosa interactome to date, collected from a variety of resources, including interactions predicted computationally to further expand analysis capabilities. The web server implementation makes it easily extendable to other bacterial species. AVAILABILITY AND IMPLEMENTATION: PaIntDB is freely available at https://www.paintdb.ca, the source code and database file are available at https://github.com/yavyx/PaIntDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systems Biology Approaches to Understanding the Human Immune System.

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

High-throughput detection of RNA processing in bacteria.

BACKGROUND: Understanding the RNA processing of an organism's transcriptome is an essential but challenging step in understanding its biology. Here we investigate with unprecedented detail the transcriptome of Pseudomonas aeruginosa PAO1, a medically important and innately multi-drug resistant bacterium. We systematically mapped RNA cleavage and dephosphorylation sites that result in 5'-monophosphate terminated RNA (pRNA) using monophosphate RNA-Seq (pRNA-Seq). Transcriptional start sites (TSS) were also mapped using differential RNA-Seq (dRNA-Seq) and both datasets were compared to conventional RNA-Seq performed in a variety of growth conditions. RESULTS: The pRNA-Seq library revealed known tRNA, rRNA and transfer-messenger RNA (tmRNA) processing sites, together with previously uncharacterized RNA cleavage events that were found disproportionately near the 5' ends of transcripts associated with basic bacterial functions such as oxidative phosphorylation and purine metabolism. The majority (97%) of the processed mRNAs were cleaved at precise codon positions within defined sequence motifs indicative of distinct endonucleolytic activities. The most abundant of these motifs corresponded closely to an E. coli RNase E site previously established in vitro. Using the dRNA-Seq library, we performed an operon analysis and predicted 3159 potential TSS. A correlation analysis uncovered 105 antiparallel pairs of TSS that were separated by 18 bp from each other and were centered on single palindromic TAT(A/T)ATA motifs (likely - 10 promoter elements), suggesting that, consistent with previous in vitro experimentation, these sites can initiate transcription bi-directionally and may thus provide a novel form of transcriptional regulation. TSS and RNA-Seq analysis allowed us to confirm expression of small non-coding RNAs (ncRNAs), many of which are differentially expressed in swarming and biofilm formation conditions. CONCLUSIONS: This study uses pRNA-Seq, a method that provides a genome-wide survey of RNA processing, to study the bacterium Pseudomonas aeruginosa and discover extensive transcript processing not previously appreciated. We have also gained novel insight into RNA maturation and turnover as well as a potential novel form of transcription regulation. NOTE: All sequence data has been submitted to the NCBI sequence read archive. Accession numbers are as follows: [NCBI sequence read archive: SRX156386, SRX157659, SRX157660, SRX157661, SRX157683 and SRX158075]. The sequence data is viewable using Jbrowse on www.pseudomonas.com .

Genotypes Associated with Listeria monocytogenes Isolates Displaying Impaired or Enhanced Tolerances to Cold, Salt, Acid, or Desiccation Stress.

The human pathogen Listeria monocytogenes is a large concern in the food industry where its continuous detection in food products has caused a string of recalls in North America and Europe. Most recognized for its ability to grow in foods during refrigerated storage, L. monocytogenes can also tolerate several other food-related stresses with some strains possessing higher levels of tolerances than others. The objective of this study was to use a combination of phenotypic analyses and whole genome sequencing to elucidate potential relationships between L. monocytogenes genotypes and food-related stress tolerance phenotypes. To accomplish this, 166 L. monocytogenes isolates were sequenced and evaluated for their ability to grow in cold (4°C), salt (6% NaCl, 25°C), and acid (pH 5, 25°C) stress conditions as well as survive desiccation (33% RH, 20°C). The results revealed that the stress tolerance of L. monocytogenes is associated with serotype, clonal complex (CC), full length inlA profiles, and the presence of a plasmid which was identified in 55% of isolates. Isolates with full length inlA exhibited significantly (p < 0.001) enhanced cold tolerance relative to those harboring a premature stop codon (PMSC) in this gene. Similarly, isolates possessing a plasmid demonstrated significantly (p = 0.013) enhanced acid tolerance. We also identified nine new L. monocytogenes sequence types, a new inlA PMSC, and several connections between CCs and the presence/absence or variations of specific genetic elements. A whole genome single-nucleotide-variants phylogeny revealed sporadic distribution of tolerant isolates and closely related sensitive and tolerant isolates, highlighting that minor genetic differences can influence the stress tolerance of L. monocytogenes. Specifically, a number of cold and desiccation sensitive isolates contained PMSCs in σB regulator genes (rsbS, rsbU, rsbV). Collectively, the results suggest that knowing the sequence type of an isolate in addition to screening for the presence of full-length inlA and a plasmid, could help food processors and food agency investigators determine why certain isolates might be persisting in a food processing environment. Additionally, increased sequencing of L. monocytogenes isolates in combination with stress tolerance profiling, will enhance the ability to identify genetic elements associated with higher risk strains.

Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database.

The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using community-based approaches including a major new Pseudomonas community initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches.

IslandViewer 3: more flexible, interactive genomic island discovery, visualization and analysis.

IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. We now report a major new release of IslandViewer, since the last release in 2013. IslandViewer 3 incorporates a completely new genome visualization tool, IslandPlot, enabling for the first time interactive genome analysis and gene search capabilities using synchronized circular, horizontal and vertical genome views. In addition, more curated virulence factors and antimicrobial resistance genes have been incorporated, and homologs of these genes identified in closely related genomes using strict filters. Pathogen-associated genes have been re-calculated for all pre-computed complete genomes. For user-uploaded genomes to be analysed, IslandViewer 3 can also now handle incomplete genomes, with an improved queuing system on compute nodes to handle user demand. Overall, IslandViewer 3 represents a significant new version of this GI analysis software, with features that may make it more broadly useful for general microbial genome analysis and visualization.

IslandViewer update: Improved genomic island discovery and visualization.

IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a web-accessible application for the computational prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin and are of high interest because they disproportionately encode virulence factors and other adaptations of medical, environmental and industrial interest. Many computational tools exist for the prediction of GIs, but three of the most accurate methods are available in integrated form via IslandViewer: IslandPath-DIMOB, SIGI-HMM and IslandPick. IslandViewer GI predictions are precomputed for all complete microbial genomes from National Center for Biotechnology Information, with an option to upload other genomes and/or perform customized analyses using different settings. Here, we report recent changes to the IslandViewer framework that have vastly improved its efficiency in handling an increasing number of users, plus better facilitate custom genome analyses. Users may also now overlay additional annotations such as virulence factors, antibiotic resistance genes and pathogen-associated genes on top of current GI predictions. Comparisons of GIs between user-selected genomes are now facilitated through a highly requested side-by-side viewer. IslandViewer improvements aim to provide a more flexible interface, coupled with additional highly relevant annotation information, to aid analysis of GIs in diverse microbial species.