Prediction of trehalose-metabolic pathway and comparative analysis of KEGG, MetaCyc, and RAST databases based on complete genome of Variovorax sp. PAMC28711

Background Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source. Results While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme (maltooligosyl-trehalose synthase, EC 5.4.99.15). The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway. Conclusions Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations.

TbasCO: Trait-based Comparative ’Omics Identifies Ecosystem-Level and Niche- Differentiating Adaptations of an Engineered Microbiome

ABSTRACTA grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while 14 genomes containing the high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by 11 genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for revealing hidden metabolic versatility when investigating genomic data alone by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

The 2021 update of the EPA’s adverse outcome pathway database

The EPA developed the Adverse Outcome Pathway Database (AOP-DB) to better characterize adverse outcomes of toxicological interest that are relevant to human health and the environment. Here we present the most recent version of the EPA Adverse Outcome Pathway Database (AOP-DB), version 2. AOP-DB v.2 introduces several substantial updates, which include automated data pulls from the AOP-Wiki 2.0, the integration of tissue-gene network data, and human AOP-gene data by population, semantic mapping and SPARQL endpoint creation, in addition to the presentation of the first publicly available AOP-DB web user interface. Potential users of the data may investigate specific molecular targets of an AOP, the relation of those gene/protein targets to other AOPs, cross-species, pathway, or disease-AOP relationships, or frequencies of AOP-related functional variants in particular populations, for example. Version updates described herein help inform new testable hypotheses about the etiology and mechanisms underlying adverse outcomes of environmental and toxicological concern.

Measuring pathway database coverage of the phosphoproteome

Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.

Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed in-silico simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

Local delivery of FTY720 induces neutrophil activation through chemokine signaling in an oronasal fistula model

Purpose Cleft palate repair surgeries lack a regenerative reconstructive option and, in many cases, develop complications including oronasal fistula (ONF). Our group has developed a novel murine phenocopy of ONF to study the oral cavity wound healing program. Using this model, our team previously identified that delivery of FTY720 on a nanofiber scaffold had a unique immunomodulatory effect directing macrophages and monocytes into a pro-regenerative state during ONF healing. Here, the objective of this study was to determine the effects of local biomaterial-based FTY720 delivery in the ONF model on the early bulk gene expression and neutrophil phenotypic response within the regenerating tissue. Methods Using a mouse model of ONF formation, a palate defect was created and was treated with FTY720 nanofiber scaffolds or (blank) vehicle control nanofibers. At 1 and 3 days post-implantation, ONF oral mucosal tissue from the defect region was collected for RNA sequencing analysis or flow cytometry. For the RNA-seq expression profiling, intracellular pathways were assessed using the KEGG Pathway database and Gene Ontology (GO) Terms enrichment interactive graph. To assess the effects of FTY720 on different neutrophil subpopulations, flow cytometry data was analyzed using pseudotime analysis based on Spanning-tree Progression Analysis of Density-normalized Events (SPADE). Results RNA sequencing analysis of palate mucosa injured tissue identified 669 genes that were differentially expressed (DE) during the first 3 days of ONF wound healing after local delivery of FTY720, including multiple genes in the sphingolipid signaling pathway. Evaluation of the DE genes at the KEGG Pathway database also identified the inflammatory immune response pathways (chemokine signaling, cytokine-cytokine receptor interaction, and leukocyte transendothelial migration), and the Gene Ontology enrichment analysis identified neutrophil chemotaxis and migration terms. SPADE dendrograms of CD11b+Ly6G+ neutrophils at both day 1 and day 3 post-injury showed significantly distinct subpopulations of neutrophils in oral mucosal defect tissue from the FTY720 scaffold treatment group compared to the vehicle control group (blank). Increased expression of CD88 and Vav1, among other genes, were found and staining of the ONF area demonstrated increased VAV1 staining in FTY720‐treated healing oral mucosa. Conclusion Treatment of oral mucosal defects using FTY720 scaffolds is a promising new immunotherapy to improve healing outcomes and reducing ONF formation during cleft palate surgical repair. Local delivery of FTY720 nanofiber scaffolds during ONF healing significantly shifted early gene transcription associated with immune cell recruitment and modulation of the immune microenvironment results in distinct neutrophil subpopulations in the oral mucosal defect tissue that provides a critical shift toward pro-regenerative immune signaling.

Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics

Background Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. Results We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. Conclusions The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.

WikiPathways: connecting communities

WikiPathways (https://www.wikipathways.org) is a biological pathway database known for its collaborative nature and open science approaches. With the core idea of the scientific community developing and curating biological knowledge in pathway models, WikiPathways lowers all barriers for accessing and using its content. Increasingly more content creators, initiatives, projects and tools have started using WikiPathways. Central in this growth and increased use of WikiPathways are the various communities that focus on particular subsets of molecular pathways such as for rare diseases and lipid metabolism. Knowledge from published pathway figures helps prioritize pathway development, using optical character and named entity recognition. We show the growth of WikiPathways over the last three years, highlight the new communities and collaborations of pathway authors and curators, and describe various technologies to connect to external resources and initiatives. The road toward a sustainable, community-driven pathway database goes through integration with other resources such as Wikidata and allowing more use, curation and redistribution of WikiPathways content.

71 Whole exome sequencing of individuals presenting extreme phenotypes of high and low-risk of developing tobacco-induced lung adenocarcinoma: relevance of immune and DNA-repair related pathways

BackgroundIndividual susceptibility to carcinogens may depend on genetic background. We performed for the first-time Whole Exome Sequencing (WES) of germline DNA from individuals presenting phenotypes of extreme sensitivity and resistance to developing tobacco-induced lung adenocarcinoma, in order to characterize the genetic background associated with these relevant phenotypes.MethodsWe performed WES of germline DNA from heavy smokers (≥15 pack-years) who either developed lung adenocarcinoma at an early age (≤55 years, extreme cases, n=50) or did not present lung adenocarcinoma or other tumors at an advanced age (≥72 years, extreme controls, n=50). We selected non-synonymous variants (missense and non-sense) located in the coding regions and consensus splice sites of the genes showing significantly different allelic frequencies between both cohorts. We validated our results in germline data from 52 additional extreme cases selected from TCGA using the same criteria (diagnosis of lung adenocarcinoma at ≤55 years, tobacco consumption ≥15 pack-years).ResultsThe mean age for the extreme cases and controls was respectively 49.7 and 77.5 years. Mean tobacco consumption was 43.5 and 54.4 pack-years. We identified 619 significantly different variants between both cohorts, and we validated 107 of these in 52 extreme cases selected from TCGA (mean age 49.3 years, mean tobacco consumption 37 pack-years). Nine validated variants, located in relevant cancer related genes, such as PARP4 (DNA repair), HLA-A (antigen presentation) or NQO1 (detoxification) among others, achieved statistical significance in the False Discovery Rate test (FDR) (table 1). The most significant validated variant (p=4.48 × 10-5) was located in the tumor-suppressor gene ALPK2. The Reactome Pathway Database analysis showed that the genes harboring the most significant validated variants were significantly related to antigen processing and presentation, interferon and cytokine signaling and immune regulation, also achieving statistical significance in the FDR test (table 2).Abstract 71 Table 1Most significant validated variants.Abstract 71 Table 2Reactome pathway database analysis of pathways related to the genes that harbor the validated variantsConclusionsWe describe for the first time genetic variants associated with extreme phenotypes of high and low-risk for the development of tobacco-induced lung adenocarcinoma, assessed with WES. The most significant validated variants were related with antigen presentation, immune regulation and DNA repair. Our results and our strategy warrant further development to characterize these clinically relevant phenotypes.Ethics ApprovalThe study was approved by the Investigational Review Board of Clinica Universidad de Navarra, approval number 021/2009.