Reactome and the Gene Ontology: Digital convergence of data resources

Small Non-Coding RNAs and Their Role in Locoregional Metastasis and Outcomes in Early-Stage Breast Cancer Patients

Deregulation of small non-coding RNAs (sncRNAs) has been associated with the onset of metastasis. We evaluated the expression of sncRNAs in patients with early-stage breast cancer, performing RNA sequencing in 60 patients for whom tumor and sentinel lymph node (SLN) samples were available, and conducting differential expression, gene ontology, enrichment and survival analyses. Sequencing annotation classified most of the sncRNAs into small nucleolar RNA (snoRNAs, 70%) and small nuclear RNA (snRNA, 13%). Our results showed no significant differences in sncRNA expression between tumor or SLNs obtained from the same patient. Differential expression analysis showed down-regulation (n = 21) sncRNAs and up-regulation (n = 2) sncRNAs in patients with locoregional metastasis. The expression of SNHG5, SNORD90, SCARNA2 and SNORD78 differentiated luminal A from luminal B tumors, whereas SNORD124 up-regulation was associated with luminal B HER2+ tumors. Discriminating analysis and receiver-operating curve analysis revealed a signature of six snoRNAs (SNORD93, SNORA16A, SNORD113-6, SNORA7A, SNORA57 and SNORA18A) that distinguished patients with locoregional metastasis and predicted patient outcome. Gene ontology and Reactome pathway analysis showed an enrichment of biological processes associated with translation initiation, protein targeting to specific cell locations, and positive regulation of Wnt and NOTCH signaling pathways, commonly involved in the promotion of metastases. Our results point to the potential of several sncRNAs as surrogate markers of lymph node metastases and patient outcome in early-stage breast cancer patients. Further preclinical and clinical studies are required to understand the biological significance of the most significant sncRNAs and to validate our results in a larger cohort of patients.

Lamin A K97E leads to NF-κB-mediated dysfunction of inflammatory responses in dilated cardiomyopathy

BACKGROUND INFORMATION

Lamins are type V intermediate filament proteins underlying the inner nuclear membrane which provide structural rigidity to the nucleus, tether the chromosomes, maintain nuclear homeostasis, and remain dynamically associated with developmentally regulated regions of the genome. A large number of mutations particularly in the LMNA gene encoding lamin A/C results in a wide array of human diseases, collectively termed as laminopathies. Dilated Cardiomyopathy (DCM) is one such laminopathic cardiovascular disease which is associated with systolic dysfunction of left or both ventricles leading to cardiac arrhythmia which ultimately culminates into myocardial infarction.

RESULTS

In this work, we have unraveled the epigenetic landscape to address the regulation of gene expression in mouse myoblast cell line in the context of the missense mutation LMNA 289A

CONCLUSIONS

We report here for the first time that there is a significant downregulation of the NF-κB pathway, which has been implicated in cardio-protection elsewhere.

SIGNIFICANCE

This provides a new pathophysiological explanation that correlates an LMNA mutation and dilated cardiomyopathy.

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Key Words

• NF‐κB signaling
• dilated cardiomyopathy
• epigenetic modifications
• interleukins
• lamin A

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MESH Headings

• Mice
• Animals
• Humans
• Cardiomyopathy, Dilated/genetics
• Cardiomyopathy, Dilated/metabolism
• Lamin Type A/genetics
• Lamin Type A/chemistry
• Lamin Type A/metabolism
• NF-kappa B/genetics
• NF-kappa B/metabolism
• Mutation
• Nuclear Lamina

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Grants

• Department of Atomic Energy, Government of India

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Affiliation(s)

Duhita Sengupta Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Kolkata, India Homi Bhabha National Institute (HBNI), Mumbai, India
Kaushik Sengupta Biophysics and Structural Genomics Division, Saha Institute of Nuclear Physics, Kolkata, India Homi Bhabha National Institute (HBNI), Mumbai, India

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Collaborators Collapse

Enrichment on steps, not genes, improves inference of differentially expressed pathways

Enrichment analysis is frequently used in combination with differential expression data to investigate potential commonalities amongst lists of genes and generate hypotheses for further experiments. However, current enrichment analysis approaches on pathways ignore the functional relationships between genes in a pathway, particularly OR logic that occurs when a set of proteins can each individually perform the same step in a pathway. As a result, these approaches miss pathways with large or multiple sets because of an inflation of pathway size (when measured as the total gene count) relative to the number of steps. We address this problem by enriching on step-enabling entities in pathways. We treat sets of protein-coding genes as single entities, and we also weight sets to account for the number of genes in them using the multivariate Fisher's noncentral hypergeometric distribution. We then show three examples of pathways that are recovered with this method and find that the results have significant proportions of pathways not found in gene list enrichment analysis.

Decoding semiotic minimal genome: a non-genocentric approach

The search for the minimum information required for an organism to sustain a cellular system network has rendered both the identification of a fixed number of known genes and those genes whose function remains to be identified. The approaches used in such search generally focus their analysis on coding genomic regions, based on the genome to proteic-product perspective. Such approaches leave other fundamental processes aside, mainly those that include higher-level information management. To cope with this limitation, a non-genocentric approach based on genomic sequence analysis using language processing tools and gene ontology may prove an effective strategy for the identification of those fundamental genomic elements for life autonomy. Additionally, this approach will provide us with an integrative analysis of the information value present in all genomic elements, regardless of their coding status.

The Reactome Pathway Knowledgebase 2024

The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.

Semantic representation of neural circuit knowledge in Caenorhabditis elegans

In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology-Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural-Circuit Causal Activity Modelling (CeN-CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase, and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a connected and well-defined way. To test whether individual genes from well-defined pathways result in similar and distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of two related but distinct pathways, gluconeogenesis and glycolysis, we can identify causal paths in gene networks that give rise to discrete phenotypic outcomes for perturbations of glycolysis and gluconeogenesis. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates

Immunogenic cell death (ICD) serves a critical role in regulating cell death adequate to activate an adaptive immune response, and it is associated with various inflammation-related diseases. However, the specific role of ICD-related genes in COVID-19 remains unclear. We acquired COVID-19-related information from the GEO database and a total of 14 ICD-related differentially expressed genes (DEGs) were identified. These ICD-related DEGs were closely associated with inflammation and immune activity. Afterward, CASP1, CD4, and EIF2AK3 among the 14 DEGs were selected as feature genes based on LASSO, Random Forest, and SVM-RFE algorithms, which had reliable diagnostic abilities. Moreover, functional enrichment analysis indicated that these feature genes may have a potential role in COVID-19 by being involved in the regulation of immune response and metabolism. Further CIBERSORT analysis demonstrated that the variations in the immune microenvironment of COVID-19 patients may be correlated with CASP1, CD4, and EIF2AK3. Additionally, 33 drugs targeting 3 feature genes had been identified, and the ceRNA network demonstrated a complicated regulative association based on these feature genes. Our work identified that CASP1, CD4, and EIF2AK3 were diagnostic genes of COVID-19 and correlated with immune activity. This study presents a reliable diagnostic signature and offers an overview to investigate the mechanism of COVID-19.

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Elevated Levels of Lamin A Promote HR and NHEJ-Mediated Repair Mechanisms in High-Grade Ovarian Serous Carcinoma Cell Line

Extensive research for the last two decades has significantly contributed to understanding the roles of lamins in the maintenance of nuclear architecture and genome organization which is drastically modified in neoplasia. It must be emphasized that alteration in lamin A/C expression and distribution is a consistent event during tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (high-grade ovarian serous carcinoma cell line) in comparison to IOSE (immortalised ovarian surface epithelial cells) and, consequently, altered damage repair machinery in OVCAR3. We have analysed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We hereby establish the role of elevated lamin A in neoplastic transformation in the context of high-grade ovarian serous cancer through a combination of HR and NHEJ mechanisms.

Differential microRNA profiles in elderly males with seborrheic dermatitis

Seborrheic dermatitis (SD) is one of the most common skin diseases characterized by inflammatory symptoms and cell proliferation, which has increased incidence in patients older than 50 years. Although the roles of microRNAs (miRNAs) have been investigated in several diseases, miRNA profiles of patients with SD remain unknown. This study aimed to identify differentially expressed miRNAs (DEMs) in lesions of elderly male patients with SD. We used a microarray-based approach to identify DEMs in lesions compared to those in non-lesions of patients with SD. Furthermore, Gene Ontology and pathway enrichment analysis were performed using bioinformatics tools to elucidate the functional significance of the target mRNAs of DEMs in lesions of patients with SD. Expression levels of two miRNAs-hsa-miR-6831-5p and hsa-miR-7107-5p-were downregulated, whereas those of six miRNAs-hsa-miR-20a-5p, hsa-miR-191-5p, hsa-miR-127-3p, hsa-miR-106b-5p, hsa-miR-342-3p, and hsa-miR-6824-5p-were upregulated. Functions of the SD-related miRNAs were predicted to be significantly associated with typical dermatological pathogenesis, such as cell proliferation, cell cycle, apoptosis, and immune regulation. In summary, SD alters the miRNA profile, and target mRNAs of the DEMs are related to immune responses and cell proliferation, which are the two main processes in SD pathogenesis.

Integration of metabolites from meta-analysis with transcriptome reveals enhanced SPHK1 in PDAC with a background of pancreatitis

BACKGROUND

Pathophysiology of transformation of inflammatory lesions in chronic pancreatitis (CP) to pancreatic ductal adenocarcinoma (PDAC) is not clear.

METHODS

We conducted a systematic review, meta-analysis of circulating metabolites, integrated this data with transcriptome analysis of human pancreatic tissues and validated using immunohistochemistry. Our aim was to establish biomarker signatures for early malignant transformation in patients with underlying CP and identify therapeutic targets.

RESULTS

Analysis of 19 studies revealed AUC of 0.86 (95% CI 0.81-0.91, P < 0.0001) for all the altered metabolites (n = 88). Among them, lipids showed higher differentiating efficacy between PDAC and CP; P-value (< 0.0001). Pathway enrichment analysis identified sphingomyelin metabolism (impact value-0.29, FDR of 0.45) and TCA cycle (impact value-0.18, FDR of 0.06) to be prominent pathways in differentiating PDAC from CP. Mapping circulating metabolites to corresponding genes revealed 517 altered genes. Integration of these genes with transcriptome data of CP and PDAC with a background of CP (PDAC-CP) identified three upregulated genes; PIGC, PPIB, PKM and three downregulated genes; AZGP1, EGLN1, GNMT. Comparison of CP to PDAC-CP and PDAC-CP to PDAC identified upregulation of SPHK1, a known oncogene.

CONCLUSIONS

Our analysis suggests plausible role for SPHK1 in development of pancreatic adenocarcinoma in long standing CP patients. SPHK1 could be further explored as diagnostic and potential therapeutic target.

On the influence of several factors on pathway enrichment analysis

Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.

Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein-protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

Rhea, the reaction knowledgebase in 2022

Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number of key developments in Rhea since our last report in the database issue of Nucleic Acids Research in 2019. These include improved reaction coverage in Rhea, the adoption of Rhea as the reference vocabulary for enzyme annotation in the UniProt knowledgebase UniProtKB (https://www.uniprot.org), the development of a new Rhea website, and the designation of Rhea as an ELIXIR Core Data Resource. We hope that these and other developments will enhance the utility of Rhea as a reference resource to study and engineer enzymes and the metabolic systems in which they function.

COVID-19 Specific Immune Markers Revealed by Single Cell Phenotypic Profiling

COVID-19 is a viral infection, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and characterized by a complex inflammatory process and clinical immunophenotypes. Nowadays, several alterations of immune response within the respiratory tracts as well as at the level of the peripheral blood have been well documented. Nonetheless, their effects on COVID-19-related cell heterogeneity and disease progression are less defined. Here, we performed a single-cell RNA sequencing of about 400 transcripts relevant to immune cell function including surface markers, in mononuclear cells (PBMCs) from the peripheral blood of 50 subjects, infected with SARS-CoV-2 at the diagnosis and 27 healthy blood donors as control. We found that patients with COVID-19 exhibited an increase in COVID-specific surface markers in different subsets of immune cell composition. Interestingly, the expression of cell receptors, such as IFNGR1 and CXCR4, was reduced in response to the viral infection and associated with the inhibition of the related signaling pathways and immune functions. These results highlight novel immunoreceptors, selectively expressed in COVID-19 patients, which affect the immune functionality and are correlated with clinical outcomes.