Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Qarles: a web server for the quick characterization of large sets of genes

The characterization of gene sets is a recurring task in computational biology. Identifying specific properties of a hit set compared to a reference set can reveal biological roles and mechanisms, and can lead to the prediction of new hits. However, collecting the features to evaluate can be time consuming, and implementing an informative but compact graphical representation of the multiple comparisons can be challenging, particularly for bench scientists. Here, I present Qarles (quick characterization of large sets of genes), a web server that annotates Saccharomyces cerevisiae gene sets by querying a database of 31 features widely used by the yeast community and that identifies their specific properties, providing publication-ready figures and reliable statistics. Qarles has a deliberately simple user interface with all the functionality in a single web page and a fast response time to facilitate adoption by the scientific community. Qarles provides a rich and compact graphical output, including up to five gene set comparisons across 31 features in a single dotplot, and interactive boxplots to enable the identification of outliers. Qarles can also predict new hit genes by using a random forest trained on the selected features. The web server is freely available at https://qarles.org.

Enrichment Analysis and Deep Learning in Biomedical Ontology: Applications and Advancements

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.

ACT2.6: Global Gene Coexpression Network in Arabidopsis thaliana Using WGCNA

BACKGROUND/OBJECTIVES

Genes with similar expression patterns across multiple samples are considered coexpressed, and they may participate in similar biological processes or pathways. Gene coexpression networks depict the degree of similarity between the expression profiles of all genes in a set of samples. Gene coexpression tools allow for the prediction of functional gene partners or the assignment of roles to genes of unknown function. Weighted Gene Correlation Network Analysis (WGCNA) is an R package that provides a multitude of functions for constructing and analyzing a weighted or unweighted gene coexpression network.

METHODS

Previously preprocessed, high-quality gene expression data of 3500 samples of Affymetrix microarray technology from various tissues of the Arabidopsis thaliana plant model species were used to construct a weighted gene coexpression network, using WGCNA.

RESULTS

The gene dendrogram was used as the basis for the creation of a new Arabidopsis coexpression tool (ACT) version (ACT2.6). The dendrogram contains 21,273 leaves, each one corresponding to a single gene. Genes that are clustered in the same clade are coexpressed. WGCNA grouped the genes into 27 functional modules, all of which were positively or negatively correlated with specific tissues.

DISCUSSION

Genes known to be involved in common metabolic pathways were discovered in the same module. By comparing the current ACT version with the previous one, it was shown that the new version outperforms the old one in discovering the functional connections between gene partners. ACT2.6 is a major upgrade over the previous version and a significant addition to the collection of public gene coexpression tools.

GeneSetCart: assembling, augmenting, combining, visualizing, and analyzing gene sets

Converting multiomics datasets into gene sets facilitates data integration that leads to knowledge discovery. Although there are tools developed to analyze gene sets, only a few offer the management of gene sets from multiple sources. GeneSetCart is an interactive web-based platform that enables investigators to gather gene sets from various sources; augment these sets with gene-gene coexpression correlations and protein-protein interactions; perform set operations on these sets such as union, consensus, and intersection; and visualize and analyze these gene sets, all in one place. GeneSetCart supports the upload of single or multiple gene sets, as well as fetching gene sets by searching PubMed for genes comentioned with terms in publications. Venn diagrams, heatmaps, Uniform Manifold Approximation and Projection (UMAP) plots, SuperVenn diagrams, and UpSet plots can visualize the gene sets in a GeneSetCart session to summarize the similarity and overlap among the sets. Users of GeneSetCart can also perform enrichment analysis on their assembled gene sets with external tools. All gene sets in a session can be saved to a user account for reanalysis and sharing with collaborators. GeneSetCart has a gene set library crossing feature that enables analysis of gene sets created from several National Institutes of Health Common Fund programs. For the top overlapping sets from pairs of programs, a large language model (LLM) is prompted to propose possible reasons for the high overlap. Using this feature, two use cases are presented. In addition, users of GeneSetCart can produce publication-ready reports from their uploaded sets. Text in these reports is also supplemented with an LLM. Overall, GeneSetCart is a useful resource enabling biologists without programming expertise to facilitate data integration for hypothesis generation.

EnrichDO: a global weighted model for Disease Ontology enrichment analysis

BACKGROUND

Disease Ontology (DO) has been widely studied in biomedical research and clinical practice to describe the roles of genes. DO enrichment analysis is an effective means to discover associations between genes and diseases. Compared to hundreds of Gene Ontology (GO)-based enrichment analysis methods, however, DO-based methods are relatively scarce, and most current DO-based approaches are term-for-term and thus are unable to solve over-enrichment problems caused by the "true-path" rule.

RESULTS

Here, we describe a novel double-weighted model, EnrichDO, which leverages the latest annotations of the human genome with DO terms and integrates DO graph topology on a global scale. Compared to classic enrichment methods (mainly for GO) and existing DO-based enrichment tools, EnrichDO performs better in both GO and DO enrichment analysis cases. It can accurately identify more specific terms, without ignoring the truly associated parent terms, as shown in the Alzheimer's disease (AD) case (AD ranked first). Moreover, both a simulated test and a data perturbation test validate the accuracy and robustness of EnrichDO. Finally, EnrichDO is applied to other types of datasets to expand its application, including gene expression profile datasets, a host gene set of microorganisms, and hallmark gene sets. Based on the findings reported here, EnrichDO shows significant improvement via all experimental results.

CONCLUSIONS

EnrichDO provides an effective DO enrichment analysis model for gaining insight into the significance of a particular gene set in the context of disease. To increase the usability of EnrichDO, we have developed an R-based software package, which is freely available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/EnrichDO.html) or at https://github.com/liangcheng-hrbmu/EnrichDO.

Exploring the role of the Rab network in epithelial-to-mesenchymal transition

Motivation

Rab GTPases (Rabs) are crucial for membrane trafficking within mammalian cells, and their dysfunction is implicated in many diseases. This gene family plays a role in several crucial cellular processes. Network analyses can uncover the complete repertoire of interaction patterns across the Rab network, informing disease research, opening new opportunities for therapeutic interventions.

Results

We examined Rabs and their interactors in the context of epithelial-to-mesenchymal transition (EMT), an indicator of cancer metastasizing to distant organs. A Rab network was first established from analysis of literature and was gradually expanded. Our Python module, resnet, assessed its network resilience and selected an optimally sized, resilient Rab network for further analyses. Pathway enrichment confirmed its role in EMT. We then identified 73 candidate genes showing a strong up-/down-regulation, across 10 cancer types, in patients with metastasized tumours compared to only primary-site tumours. We suggest that their encoded proteins might play a critical role in EMT, and further in vitro studies are needed to confirm their role as predictive markers of cancer metastasis. The use of resnet within the systematic analysis approach described here can be easily applied to assess other gene families and their role in biological events of interest.

Availability and implementation

Source code for resnet is freely available at https://github.com/Unmani199/resnet.

BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

NETWORK ANALYSIS OF SINGLE-NUCLEOTIDE POLYMORPHISMS ASSOCIATED WITH ABERRANT INFLAMMATION IN TRAUMA PATIENTS SUGGESTS A ROLE FOR VESICLE-ASSOCIATED INFLAMMATORY PROGRAMS INVOLVING CD55

ABSTRACT

Background: Critical illness stemming from severe traumatic injury is a leading cause of morbidity and mortality worldwide and involves the dysfunction of multiple organ systems, driven, at least in part, by dysregulated inflammation. We and others have shown a key role for genetic predisposition to dysregulated inflammation and downstream adverse critical illness outcomes. Recently, we demonstrated an association among genotypes at the single-nucleotide polymorphism (SNP) rs10404939 in LYPD4 , dysregulated systemic inflammation, and adverse clinical outcomes in a broad sample of ~1,000 critically ill patients. Methods: We sought to gain mechanistic insights into the role of LYPD4 in critical illness by bioinformatically analyzing potential interactions among rs10404939 and other SNPs. We analyzed a dataset of common (i.e., not rare) SNPs previously defined to be associated with genotype-specific, significantly dysregulated systemic inflammation trajectories in trauma patients, in comparison to a control dataset of common SNPs determined to exhibit an absence of genotype-specific inflammatory responses. Results: In the control dataset, this analysis implicated SNPs associated with phosphatidylinositol and various membrane transport proteins, but not LYPD4. In the patient subset with genotypically dysregulated inflammation, our analysis suggested the co-localization to lipid rafts of LYPD4 and the complement receptor CD55, as well as the neurally related CNTNAP2 and RIMS4. Segregation of trauma patients based on genotype of the CD55 SNP rs11117564 showed distinct trajectories of organ dysfunction and systemic inflammation despite similar demographics and injury characteristics. Conclusion: These analyses define novel interactions among SNPs that could enhance our understanding of the response to traumatic injury and critical illness.

Molecular signature of immune-related new survival predictions for subtype of renal cell carcinomas

Background

Kidney renal papillary cell carcinoma (KIRP), kidney chromophobe (KICH), and kidney renal clear cell carcinoma (KIRC) are three most common subtypes of renal cell carcinomas (RCC), and its development is a multifaceted process that intricately involves the interplay of numerous genes. Despite recent advances in research on renal cell carcinoma, the prognosis of KIRC patients remains dismal. Therefore, there is an urgent need to explore new prognostic biomarkers and treatment strategies to help clinicians choose more effective treatment methods and accurately predict long-term efficacy. Our study aimed to systematically evaluate the gene expression profiles of three RCC subtypes, especially KIRC, and to identify survival-related biomarker.

Methods

In our present study, we systematically evaluate the genes expression profile difference among three subtypes of RCC, and identify the survival-related key genes signature based on GEPIA2. GeneMANIA was used to identify the functionality-related differentially expressed genes (DEGs). Furthermore, focusing on KIRC, we intersected functionality-related and survival-related DEGs based on two datasets.

Results

We ascertained five DEGs (ANK3, FREM2, KIF13B, MPP7 and SOX6) as key survival-related genes in KIRC. High levels of these five DEGs expressions were strongly associated with favorable prognosis, but not correlated to metastasis. Downregulation of these five DEGs expressions was closely associated with immunomodulators, chemokines, and infiltrating levels of different immune cells, which indicated that these five DEGs were key immune-related novel prognostic biomarkers for KIRC.

Conclusions

The five identified DEGs serve as potential novel prognostic biomarkers for KIRC. However, the crucial factors that lead to the downregulation and functional inactivation of these five key genes need to be explored in future studies.

A systematic review of mesenchymal stem cell secretome: Functional annotations, gene clusters and proteomics analyses for bone formation

The regenerative capacity of mesenchymal stem cells (MSCs) is now attributed to their ability to release paracrine factors into the extracellular matrix that boost tissue regeneration, reduce inflammation and encourage healing. Understanding the MSC secretome is crucial for shifting the prototypic conventional cell-based therapies to cell-free regenerative treatments. This systematic review aimed to analyse the functional annotations of the secretome of human adult adipose tissue and bone marrow MSCs and unveil the gene clusters responsible for bone formation. Bioinformatics tools were used to identify the biological processes, molecular functions, hallmarks and KEGG pathways of adipose and bone marrow MSC secretome proteins. We found a substantial overlap in the functional annotations and protein compositions of both adipose and bone marrow MSC secretome indicating that MSC source may be noninfluencial with regards to tissue regeneration. Additionally, a novel network pharmacology-based analysis of the secreted proteins revealed that the commonly secreted proteins within a single source interact with multiple drugable targets of bone diseases and regulate various KEGG pathway. This study unravels the secretome profile of human adult adipose and bone marrow MSCs based on the current literature and provides valuable insights into the therapeutic use of the MSC secretome for cell-free therapies.

A systematic review and meta-analysis of proteomic and metabolomic alterations in anaphylaxis reactions

Background

Anaphylaxis manifests as a severe immediate-type hypersensitivity reaction initiated through the immunological activation of target B-cells by allergens, leading to the release of mediators. However, the well-known underlying pathological mechanisms do not fully explain the whole variety of clinical and immunological presentations. We performed a systemic review of proteomic and metabolomic studies and analyzed the extracted data to improve our understanding and identify potential new biomarkers of anaphylaxis.

Methods

Proteomic and metabolomic studies in both human subjects and experimental models were extracted and selected through a systematic search conducted on databases such as PubMed, Scopus, and Web of Science, up to May 2023.

Results

Of 137 retrieved publications, we considered 12 for further analysis, including seven on proteome analysis and five on metabolome analysis. A meta-analysis of the four human studies identified 118 proteins with varying expression levels in at least two studies. Beside established pathways of mast cells and basophil activation, functional analysis of proteomic data revealed a significant enrichment of biological processes related to neutrophil activation and platelet degranulation and metabolic pathways of arachidonic acid and icosatetraenoic acid. The pathway analysis highlighted also the involvement of neutrophil degranulation, and platelet activation. Metabolome analysis across different models showed 13 common metabolites, including arachidonic acid, tryptophan and lysoPC(18:0) lysophosphatidylcholines.

Conclusion

Our review highlights the underestimated role of neutrophils and platelets in the pathological mechanisms of anaphylactic reactions. These findings, derived from a limited number of publications, necessitate confirmation through human studies with larger sample sizes and could contribute to the development of new biomarkers for anaphylaxis.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024506246.

APRF1 Interactome Reveals HSP90 as a New Player in the Complex That Epigenetically Regulates Flowering Time in Arabidopsis thaliana

WD40 repeat proteins (WDRs) are present in all eukaryotes and include members that are implicated in numerous cellular activities. They act as scaffold proteins and thus as molecular "hubs" for protein-protein interactions, which mediate the assembly of multifunctional complexes that regulate key developmental processes in Arabidopsis thaliana, such as flowering time, hormonal signaling, and stress responses. Despite their importance, many aspects of their putative functions have not been elucidated yet. Here, we show that the late-flowering phenotype of the anthesis promoting factor 1 (aprf1) mutants is temperature-dependent and can be suppressed when plants are grown under mild heat stress conditions. To gain further insight into the mechanism of APRF1 function, we employed a co-immunoprecipitation (Co-IP) approach to identify its interaction partners. We provide the first interactome of APRF1, which includes proteins that are localized in several subcellular compartments and are implicated in diverse cellular functions. The dual nucleocytoplasmic localization of ARRF1, which was validated through the interaction of APRF1 with HEAT SHOCK PROTEIN 1 (HSP90.1) in the nucleus and with HSP90.2 in the cytoplasm, indicates a dynamic and versatile involvement of APRF1 in multiple biological processes. The specific interaction of APRF1 with the chaperon HSP90.1 in the nucleus expands our knowledge regarding the epigenetic regulation of flowering time in A. thaliana and further suggests the existence of a delicate thermoregulated mechanism during anthesis.