EnrichNet: network-based gene set enrichment analysis

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.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

A Proteomic Investigation to Discover Candidate Proteins Involved in Novel Mechanisms of 5-Fluorouracil Resistance in Colorectal Cancer

One of the main obstacles to therapeutic success in colorectal cancer (CRC) is the development of acquired resistance to treatment with drugs such as 5-fluorouracil (5-FU). Whilst some resistance mechanisms are well known, it is clear from the stasis in therapy success rate that much is still unknown. Here, a proteomics approach is taken towards identification of candidate proteins using 5-FU-resistant sublines of human CRC cell lines generated in house. Using a multiplexed stable isotope labelling with amino acids in cell culture (SILAC) strategy, 5-FU-resistant and equivalently passaged sensitive cell lines were compared to parent cell lines by growing in Heavy medium with 2D liquid chromatography and Orbitrap Fusion™ Tribrid™ Mass Spectrometry analysis. Among 3003 commonly quantified proteins, six (CD44, APP, NAGLU, CORO7, AGR2, PLSCR1) were found up-regulated, and six (VPS45, RBMS2, RIOK1, RAP1GDS1, POLR3D, CD55) down-regulated. A total of 11 of the 12 proteins have a known association with drug resistance mechanisms or role in CRC oncogenesis. Validation through immunodetection techniques confirmed high expression of CD44 and CD63, two known drug resistance mediators with elevated proteomics expression results. The information revealed by the sensitivity of this method warrants it as an important tool for elaborating the complexity of acquired drug resistance in CRC.

Structural and Pharmacological Network Focused on MiRNAs Involved in Rheumatoid Arthritis: A Systematic Review

BACKGROUND

Rheumatoid Arthritis (RA) is a chronic autoimmune disease that has a prevalence of over one percent of the world population, causing substantial pain, joint deformity, and functional disability in patients. The identification and measurement of miRNAs are relatively easy to perform. Future studies will corroborate if miRNAs can fulfill their roles as biomarkers with either predictive or diagnostic evaluation of treatment potential and provide actual clinical utility.

METHODS

In the last decade, various advances have been made regarding the identification of the origin and exact functions of miRNAs, allowing us to have a potential use both in the research and clinical fields.

OBJECTIVE

This systematic review aimed to collect, analyze, and improve the current understanding of RA-related miRNAs and their applicability in therapeutics. A bibliographic search of the miRNAs involved in RA was carried out, and through the use of databases, their target genes and small molecules that had some relationship with their expression were searched. The analysis of these data was done through structural network analysis.

RESULTS

During the network analysis, miR-30a, miR-30c, let-7a, miR-144, miR-17-5p, miR-124, miR -23b, miR-23, miR-15a, miR-16 were the most connected, which could be used as possible biomarkers or be candidates for further analysis due to their interaction with other miRNAs and genes.

CONCLUSION

Additionally, this is the first systematic review, in which we proposed that small compounds like toxicants and drugs could have a potential role within RA because they regulate the expression of miRNAs involved in this pathology. Some of these compounds are commonly found as environmental contaminants, and others as drugs. These ideas open a new panorama of understanding RA, proposing possible causes or treatments against this pathology. Therefore, these small molecules would give us some indication of a relationship with RA, thereby helping in seeking causes, treatment, or prevention of this disease.

CONCLUSION

This is the first time it is intended to use structural network analysis to determine possible biomarkers of AR for diagnosis and prognosis through the expression of these miRNAs and their relationship with compounds of daily life.

Selection of Mexican Medicinal Plants by Identification of Potential Phytochemicals with Anti-Aging, Anti-Inflammatory, and Anti-Oxidant Properties through Network Analysis and Chemoinformatic Screening

Many natural products have been acquired from plants for their helpful properties. Medicinal plants are used for treating a variety of pathologies or symptoms. The axes of many pathological processes are inflammation, oxidative stress, and senescence. This work is focused on identifying Mexican medicinal plants with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects through network analysis and chemoinformatic screening of their phytochemicals. We used computational methods to analyze drug-like phytochemicals in Mexican medicinal plants, multi-target compounds, and signaling pathways related to anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence mechanisms. A total of 1373 phytochemicals are found in 1025 Mexican medicinal plants, and 148 compounds showed no harmful functionalities. These compounds displayed comparable structures with reference molecules. Based on their capacity to interact with pharmacological targets, three clusters of Mexican medicinal plants have been established. Curatella americana, Ximenia americana, Malvastrum coromandelianum, and Manilkara zapota all have anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Plumeria rubra, Lonchocarpus yucatanensis, and Salvia polystachya contained phytochemicals with anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence reported activity. Lonchocarpus guatemalensis, Vallesia glabra, Erythrina oaxacana, and Erythrina sousae have drug-like phytochemicals with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Between the drug-like phytochemicals, lonchocarpin, vallesine, and erysotrine exhibit potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. For the first time, we conducted an initial virtual screening of selected Mexican medicinal plants, which was subsequently confirmed in vivo, evaluating the anti-inflammatory activity of Lonchocarpus guatemalensis Benth in mice.

A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Chemicals specifically targeting senescent cells, so-called senolytics, constitute a promising avenue for improved cancer treatment in combination with targeted therapies. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCell-based integration of bulk and single-cell RNA-seq data identifies biological processes perturbed during senescence and predicts 90 new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

A computational model of current control mechanism for long-term potentiation (LTP) in human episodic memory based on gene-gene interaction

The establishment of long-term potentiation (LTP) is a prime process for the formation of episodic memory. During the establishment of LTP, activations of various components are required in the signaling cascade of the LTP pathway. Past efforts to determine the activation of components relied extensively on the cellular or molecular level. In this paper, we have proposed a computational model based on gene-level cascading and interaction in LTP signaling for the establishment and control of current signals for achieving the desired level of activation in the formation of episodic memory. This paper also introduces a model for a generalized signaling pathway in episodic memory. A back-propagation feedback mechanism is used for updating the interaction levels in the signaling cascade starting from the last stage and ending at the start stage of the signaling cascade. Simulation of the proposed model has been performed for the LTP signaling pathway in the context of human episodic memory. We found through simulation that the qualifying genes correction factors of all stages are updated to their maximum limit. The article explains the signaling pathway for episodic memory and proves its effectiveness through simulation results.

RichPathR: a gene set enrichment analysis and visualization tool

Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologies generates the vast number of gene lists that are up or down regulated together. One way of getting meaningful insights of the relationship of these genes is utilizing existing knowledge bases linking them with biological functions or phenotypes. Multiple public databases with annotated gene sets are available for GSEA, and enrichR is the most popular web application still requiring custom tools for large-scale mining. richPathR package is a collection of R functions that helps researchers carry out exploratory analysis and visualization of gene set enrichment using EnrichR.

Community detection in Epstein-Barr virus associated carcinomas and role of tyrosine kinase in etiological mechanisms for oncogenesis

BACKGROUND

Epstein-Barr virus (EBV) affects more than 90% of global population. The role of the virus in causing infectious mononucleosis (IM) affecting B-cells and epithelial cells and in the development of EBV associated cancers is well documented. Investigating the associated interactions can pave way for the discovery of novel therapeutic targets for EBV associated lymphoproliferative (Burkitt's Lymphoma and Hodgkin's Lymphoma) and non-lymphoproliferative diseases (Gastric cancer and Nasopharyngeal cancer).

METHODS

Based on the DisGeNET (v7.0) data set, we constructed a disease-gene network to identify genes that are involved in various carcinomas, viz. Gastric cancer (GC), Nasopharyngeal cancer (NPC), Hodgkin's lymphoma (HL) and Burkitt's lymphoma (BL). We identified communities in the disease-gene network and performed functional enrichment using over-representation analysis to detect significant biological processes/pathways and the interactions between them.

RESULT

We identified the modular communities to explore the relation of this common causative pathogen (EBV) with different carcinomas such as GC, NPC, HL and BL. Through network analysis we identified the top 10 genes linked with EBV associated carcinomas as CASP10, BRAF, NFKBIA, IFNA2, GSTP1, CSF3, GATA3, UBR5, AXIN2 and POLE. Further, the tyrosine-protein kinase (ABL1) gene was significantly over-represented in 3 out of 9 critical biological processes, viz. in regulatory pathways in cancer, the TP53 network and the Imatinib and chronic myeloid leukemia biological processes. Consequently, the EBV pathogen appears to target critical pathways involved in cellular growth arrest/apoptosis. We make our case for BCR-ABL1 tyrosine-kinase inhibitors (TKI) for further clinical investigations in the inhibition of BCR-mediated EBV activation in carcinomas for better prognostic and therapeutic outcomes.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19

MOTIVATION

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods.

RESULTS

We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19.

AVAILABILITY AND IMPLEMENTATION

GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

Pathway expression analysis

This paper introduces a pathway expression framework as an approach for constructing derived biomarkers. The pathway expression framework incorporates the biological connections of genes leading to a biologically relevant model. Using this framework, we distinguish between shedding subjects post-infection and all subjects pre-infection in human blood transcriptomic samples challenged with various respiratory viruses: H1N1, H3N2, HRV (Human Rhinoviruses), and RSV (Respiratory Syncytial Virus). Additionally, pathway expression data is used for selecting discriminatory pathways from these experiments. The classification results and selected pathways are benchmarked against standard gene expression based classification and pathway ranking methodologies. We find that using the pathway expression data along with selected pathways, which have minimal overlap with high ranking pathways found by traditional methods, improves classification rates across experiments.

circGPA: circRNA functional annotation based on probability-generating functions

Recent research has already shown that circular RNAs (circRNAs) are functional in gene expression regulation and potentially related to diseases. Due to their stability, circRNAs can also be used as biomarkers for diagnosis. However, the function of most circRNAs remains unknown, and it is expensive and time-consuming to discover it through biological experiments. In this paper, we predict circRNA annotations from the knowledge of their interaction with miRNAs and subsequent miRNA-mRNA interactions. First, we construct an interaction network for a target circRNA and secondly spread the information from the network nodes with the known function to the root circRNA node. This idea itself is not new; our main contribution lies in proposing an efficient and exact deterministic procedure based on the principle of probability-generating functions to calculate the p-value of association test between a circRNA and an annotation term. We show that our publicly available algorithm is both more effective and efficient than the commonly used Monte-Carlo sampling approach that may suffer from difficult quantification of sampling convergence and subsequent sampling inefficiency. We experimentally demonstrate that the new approach is two orders of magnitude faster than the Monte-Carlo sampling, which makes summary annotation of large circRNA files feasible; this includes their reannotation after periodical interaction network updates, for example. We provide a summary annotation of a current circRNA database as one of our outputs. The proposed algorithm could be generalized towards other types of RNA in way that is straightforward.

Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration

The creation of novel anticancer treatments for a variety of human illnesses, including different malignancies and dangerous microbes, also potentially depends on nanoparticles including silver. Recently, it has been successful to biologically synthesize metal nanoparticles using plant extracts. The natural flavonoid 3,3′, 4′, 5,5′, and 7 hexahydroxyflavon (myricetin) has anticancer properties. There is not much known about the regulatory effects of myricetin on the possible cell fate-determination mechanisms (such as apoptosis/proliferation) in colorectal cancer. Because the majority of investigations related to the anticancer activity of myricetin have dominantly focused on the enhancement of tumor cell uncontrolled growth (i.e., apoptosis). Thus, we have decided to explore the potential myricetin interactors and the associated biological functions by using an in-silico approach. Then, we focused on the main goal of the work which involved the synthesis of silver nanoparticles and the labeling of myricetin with it. The synthesized silver nanoparticles were examined using UV-visible spectroscopy, dynamic light scattering spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscopy. In this study, we have investigated the effects of myricetin on colorectal cancer where numerous techniques were used to show myricetin’s effect on colon cancer cells. Transmission Electron Microscopy was employed to monitor morphological changes. Furthermore, we have combined the results of the colorectal cancer gene expression dataset with those of the myricetin interactors and pathways. Based on the results, we conclude that myricetin is able to efficiently kill human colorectal cancer cell lines. Since, it shares important biological roles and possible route components and this myricetin may be a promising herbal treatment for colorectal cancer as per an in-silico analysis of the TCGA dataset.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks

BACKGROUND

Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules. However, these methods are not adapted to deal with multi-omics data nor consider topological features. When deriving very large modules or ignoring the broader network context, interpretability remains limited. To address these issues, we developed a SUbgraph BAsed mulTi-OMIcs Clustering framework (SUBATOMIC), which infers small and interpretable modules with a specific topology while keeping track of connections to other modules and regulators.

RESULTS

SUBATOMIC groups specific molecular interactions in composite network subgraphs of two and three nodes and clusters them into topological modules. These are functionally annotated, visualized and overlaid with expression profiles to go from static to dynamic modules. To preserve the larger network context, SUBATOMIC investigates statistically the connections in between modules as well as between modules and regulators such as miRNAs and transcription factors. We applied SUBATOMIC to analyze a composite Homo sapiens network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from different databases. We derived and annotated 5586 modules with diverse topological, functional and regulatory properties. We created novel functional hypotheses for unannotated genes. Furthermore, we integrated modules with condition specific expression data to study the influence of hypoxia in three cancer cell lines. We developed two prioritization strategies to identify the most relevant modules in specific biological contexts: one considering GO term enrichments and one calculating an activity score reflecting the degree of differential expression. Both strategies yielded modules specifically reacting to low oxygen levels.

CONCLUSIONS

We developed the SUBATOMIC framework that generates interpretable modules from integrated multi-omics networks and applied it to hypoxia in cancer. SUBATOMIC can infer and contextualize modules, explore condition or disease specific modules, identify regulators and functionally related modules, and derive novel gene functions for uncharacterized genes. The software is available at https://github.com/CBIGR/SUBATOMIC .

Identifying cellular cancer mechanisms through pathway-driven data integration

MOTIVATION

Cancer is a genetic disease in which accumulated mutations of driver genes induce a functional reorganization of the cell by reprogramming cellular pathways. Current approaches identify cancer pathways as those most internally perturbed by gene expression changes. However, driver genes characteristically perform hub roles between pathways. Therefore, we hypothesize that cancer pathways should be identified by changes in their pathway-pathway relationships.

RESULTS

To learn an embedding space that captures the relationships between pathways in a healthy cell, we propose pathway-driven non-negative matrix tri-factorization. In this space, we determine condition-specific (i.e. diseased and healthy) embeddings of pathways and genes. Based on these embeddings, we define our 'NMTF centrality' to measure a pathway's or gene's functional importance, and our 'moving distance', to measure the change in its functional relationships. We combine both measures to predict 15 genes and pathways involved in four major cancers, predicting 60 gene-cancer associations in total, covering 28 unique genes. To further exploit driver genes' tendency to perform hub roles, we model our network data using graphlet adjacency, which considers nodes adjacent if their interaction patterns form specific shapes (e.g. paths or triangles). We find that the predicted genes rewire pathway-pathway interactions in the immune system and provide literary evidence that many are druggable (15/28) and implicated in the associated cancers (47/60). We predict six druggable cancer-specific drug targets.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at: https://gitlab.bsc.es/swindels/pathway_driven_nmtf.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein-protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI ("mostDILI") and the ones that are not likely to cause DILI ("noDILI"). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

Assessing Gene Expression Related to Cisplatin Resistance in Human Oral Squamous Cell Carcinoma Cell Lines

Cisplatin-based chemotherapy has been effectively used to treat oral cancer, but treatment often fails owing to the development of drug resistance. However, the important gene expression alterations associated with these resistances remain unclear. In this study, we aimed to identify the gene expressions related to cisplatin resistance in oral squamous cell carcinoma (OSCC) cell lines. RNA samples were obtained from three cisplatin-resistant (YD-8/CIS, YD-9/CIS, and YD-38/CIS) and -sensitive (YD-8, YD-9, and YD-38) cell lines. Global gene expression was analyzed using RNA sequencing (RNA-Seq). Differentially expressed genes were determined. Based on the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, functional enrichment and signaling pathways analyses were performed. Candidate genes selected from RNA-Seq analysis were validated by quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The YD-8/CIS and YD-9/CIS samples had very similar expression patterns. qRT-PCR analysis was performed on selected genes commonly expressed between the two samples. The expression levels of 11 genes were changed in cisplatin-resistant samples compared with their parental samples; several of these genes were related to cell adhesion molecules and proteoglycans in cancer pathways. Our data provide candidate genes associated with cisplatin resistance in OSCC, but further study is required to determine which genes have an important role. Nevertheless, these results may provide new ideas to improve the clinical therapeutic outcomes of OSCC.

Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis

Functional analysis of gene sets derived from experiments is typically done by pathway annotation. Although many algorithms exist for analyzing the association between a gene set and a pathway, an issue which is generally ignored is that gene sets often represent multiple pathways. In such cases an association to a pathway is weakened by the presence of genes associated with other pathways. A way to counteract this is to cluster the gene set into more homogenous parts before performing pathway analysis on each module. We explored whether network-based pre-clustering of a query gene set can improve pathway analysis. The methods MCL, Infomap, and MGclus were used to cluster the gene set projected onto the FunCoup network. We characterized how well these methods are able to detect individual pathways in multi-pathway gene sets, and applied each of the clustering methods in combination with four pathway analysis methods: Gene Enrichment Analysis, BinoX, NEAT, and ANUBIX. Using benchmarks constructed from the KEGG pathway database we found that clustering can be beneficial by increasing the sensitivity of pathway analysis methods and by providing deeper insights of biological mechanisms related to the phenotype under study. However, keeping a high specificity is a challenge. For ANUBIX, clustering caused a minor loss of specificity, while for BinoX and NEAT it caused an unacceptable loss of specificity. GEA had very low sensitivity both before and after clustering. The choice of clustering method only had a minor effect on the results. We show examples of this approach and conclude that clustering can improve overall pathway annotation performance, but should only be used if the used enrichment method has a low false positive rate.

Characterization and Validation of ceRNA-Mediated Pathway–Pathway Crosstalk Networks Across Eight Major Cardiovascular Diseases

Pathway analysis is considered as an important strategy to reveal the underlying mechanisms of diseases. Pathways that are involved in crosstalk can regulate each other and co-regulate downstream biological processes. Furthermore, some genes in the pathways can function with other genes via the relationship of the competing endogenous RNA (ceRNA) mechanism, which has also been demonstrated to play key roles in cellular biology. However, the comprehensive analysis of ceRNA-mediated pathway crosstalk is lacking. Here, we constructed the landscape of the ceRNA-mediated pathway–pathway crosstalk of eight major cardiovascular diseases (CVDs) based on sequencing data from ∼2,800 samples. Some common features shared by numerous CVDs were uncovered. A fraction of the pathway–pathway crosstalk was conserved in multiple CVDs and a core pathway–pathway crosstalk network was identified, suggesting the similarity of pathway–pathway crosstalk among CVDs. Experimental evidence also demonstrated that the pathway crosstalk was functioned in CVDs. We split all hub pathways of each pathway–pathway crosstalk network into three categories, namely, common hubs, differential hubs, and specific hubs, which could highlight the common or specific biological mechanisms. Importantly, after a comparison analysis of the hub pathways of networks, ∼480 hub pathway-induced common modules were identified to exert functions in CVDs broadly. Moreover, we performed a random walk algorithm on the hub pathway-induced sub-network and identified 23 potentially novel CVD-related pathways. In summary, our study revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway–pathway crosstalk levels and provided a novel routine to investigate the pathway–pathway crosstalk in cardiology. All CVD pathway–pathway crosstalks are provided in http://www.licpathway.net/cepathway/index.html.

Reconstruction and Exploratory Analysis of mTORC1 Signaling Pathway and Its Applications to Various Diseases Using Network-Based Approach

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

Network Crosstalk as a Basis for Drug Repurposing

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.

Positive Selection Drives the Adaptive Evolution of Mitochondrial Antiviral Signaling (MAVS) Proteins-Mediating Innate Immunity in Mammals

The regulated production of filamentous protein complexes is essential in many biological processes and provides a new paradigm in signal transmission. The mitochondrial antiviral signaling protein (MAVS) is a critical signaling hub in innate immunity that is activated when a receptor induces a shift in the globular caspase activation and recruitment domain of MAVS into helical superstructures (filaments). It is of interest whether adaptive evolution affects the proteins involved in innate immunity. Here, we explore and confer the role of selection and diversification on mitochondrial antiviral signaling protein in mammalian species. We obtined the MAVS proteins of mammalian species and examined their differences in evolutionary patterns. We discovered evidence for these proteins being subjected to substantial positive selection. We demonstrate that immune system proteins, particularly those encoding recognition proteins, develop under positive selection using codon-based probability methods. Positively chosen regions within recognition proteins cluster in domains involved in microorganism recognition, implying that molecular interactions between hosts and pathogens may promote adaptive evolution in the mammalian immune systems. These significant variations in MAVS development in mammalian species highlights the involvement of MAVS in innate immunity. Our findings highlight the significance of accounting for how non-synonymous alterations affect structure and function when employing sequence-level studies to determine and quantify positive selection.

Angiogenesis goes computational – The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data

Network biology has become a key tool in unravelling the mechanisms of complex diseases. Detecting dys-regulated subnetworks from molecular networks is a task that needs efficient computational methods. In this work, we constructed an integrated network using gene interaction data as well as protein–protein interaction data of differentially expressed genes derived from the microarray gene expression data. We considered the level of differential expression as well as the topological weight of proteins in interaction network to quantify dys-regulation. Then, a nature-inspired Smell Detection Agent (SDA) optimisation algorithm is designed with multiple agents traversing through various paths in the network. Finally, the algorithm provides a maximum weighted module as the optimum dys-regulated subnetwork. The analysis is performed for samples of triple-negative breast cancer as well as colorectal cancer. Biological significance analysis of module genes is also done to validate the results. The breast cancer subnetwork is found to contain (i) valid biomarkers including PIK3CA, PTEN, BRCA1, AR and EGFR; (ii) validated drug targets TOP2A, CDK4, HDAC1, IL6, BRCA1, HSP90AA1 and AR; (iii) synergistic drug targets EGFR and BIRC5. Moreover, based on the weight values assigned to nodes in the subnetwork, PLK1, CTNNB1, IGF1, AURKA, PCNA, HSPA4 and GAPDH are proposed as drug targets for further studies. For colorectal cancer module, the analysis revealed the occurrence of approved drug targets TYMS, TOP1, BRAF and EGFR. Considering the higher weight values, HSP90AA1, CCNB1, AKT1 and CXCL8 are proposed as drug targets for experimentation. The derived subnetworks possess cancer-related pathways as well. The SDA-derived breast cancer subnetwork is compared with that of tools such as MCODE and Minimum Spanning Tree, and observed a higher enrichment (75%) of significant elements. Thus, the proposed nature-inspired algorithm is a novel approach to derive the optimum dys-regulated subnetwork from huge molecular network.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Extracellular vesicle proteomes of two transmissible cancers of Tasmanian devils reveal tenascin-C as a serum-based differential diagnostic biomarker

The iconic Tasmanian devil (Sarcophilus harrisii) is endangered due to the transmissible cancer Devil Facial Tumour Disease (DFTD), of which there are two genetically independent subtypes (DFT1 and DFT2). While DFT1 and DFT2 can be differentially diagnosed using tumour biopsies, there is an urgent need to develop less-invasive biomarkers that can detect DFTD and distinguish between subtypes. Extracellular vesicles (EVs), the nano-sized membrane-enclosed vesicles present in most biofluids, represent a valuable resource for biomarker discovery. Here, we characterized the proteome of EVs from cultured DFTD cells using data-independent acquisition-mass spectrometry and an in-house spectral library of > 1500 proteins. EVs from both DFT1 and DFT2 cell lines expressed higher levels of proteins associated with focal adhesion functions. Furthermore, hallmark proteins of epithelial-mesenchymal transition were enriched in DFT2 EVs relative to DFT1 EVs. These findings were validated in EVs derived from serum samples, revealing that the mesenchymal marker tenascin-C was also enriched in EVs derived from the serum of devils infected with DFT2 relative to those infected with DFT1 and healthy controls. This first EV-based investigation of DFTD increases our understanding of the cancers' EVs and their possible involvement in DFTD progression, such as metastasis. Finally, we demonstrated the potential of EVs to differentiate between DFT1 and DFT2, highlighting their potential use as less-invasive liquid biopsies for the Tasmanian devil.

Temporal and sequential order of nonoverlapping gene networks unraveled in mated female Drosophila

Mating triggers successive waves of temporal transcriptomic changes within independent gene networks in female Drosophila, suggesting a recruitment of interconnected modules that vanish in late life.

In this study, we reanalyzed available datasets of gene expression changes in female Drosophila head induced by mating. Mated females present metabolic phenotypic changes and display behavioral characteristics that are not observed in virgin females, such as repulsion to male sexual aggressiveness, fidelity to food spots selected for oviposition, and restriction to the colonization of new niches. We characterize gene networks that play a role in female brain plasticity after mating using AMINE, a novel algorithm to find dysregulated modules of interacting genes. The uncovered networks of altered genes revealed a strong specificity for each successive period of life span after mating in the female head, with little conservation between them. This finding highlights a temporal order of recruitment of waves of interconnected genes which are apparently transiently modified: the first wave disappears before the emergence of the second wave in a reversible manner and ends with few consolidated gene expression changes at day 20. This analysis might document an extended field of a programmatic control of female phenotypic traits by male seminal fluid.

Proteomics: Concepts and applications in human medicine

Proteomics is the complete evaluation of the function and structure of proteins to understand an organism’s nature. Mass spectrometry is an essential tool that is used for profiling proteins in the cell. However, biomarker discovery remains the major challenge of proteomics because of their complexity and dynamicity. Therefore, combining the proteomics approach with genomics and bioinformatics will provide an understanding of the information of biological systems and their disease alteration. However, most studies have investigated a small part of the proteins in the blood. This review highlights the types of proteomics, the available proteomic techniques, and their applications in different research fields.

Gene expression profiling utilizing extremely sensitive CDNA arrays and enrichment-based network study of major bone cancer genes

Background:

The gene interaction network is a set of genes interconnected by functional interactions among the genes. The gene interaction networks are studied to determine pathways and regulatory mechanisms in model organisms. In this research, the enrichment study of bone cancer-causing genes is undertaken to identify several hub genes associated to the development of bone cancer.

Materials and Methods:

Data on bone cancer is obtained from mutated gene samples; highly mutated genes are selected for the enrichment analysis. Due to certain interactions with each other the interaction network model for the hub genes is developed and simulations are produced to determine the levels of expression. For the array analyses, a total of 100 tumor specimens are collected. Cell cultures are prepared, RNA is extracted, cDNA arrays probes are generated, and the expressions analysis of Hub genes is determined.

Results:

Out of cDNA array findings, only 7 genes: CDKN2A, AKT1, NRAS, PIK3CA, RB1, BRAF, and TP53 are differentially expressed and shown as significant in the development of bone tumors, approximately 15 pathways have been identified, including pathways for non-small cell lung cancer, prostate cancer, pancreatic cancer, chronic myeloid leukemia, and glioma, consisting of all the identified 7 genes. After clinical validations of tumor samples, the IDH1 and TP53 gene revealed significant number of mutations similar to other genes. Specimens analysis showed that RB1, P53, and NRAS are amplified in brain tumor, while BRAF, CDKN2A, and AKT1 are amplified in sarcoma. Maximum deletion mutations of the PIK3CA gene are observed in leukemia. CDKN2A gene amplifications have been observed in virtually all tumor specimens.

Conclusion:

This study points to a recognizable evidence of novel superimposed pathways mechanisms strongly linked to cancer.

ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

DecoPath: a web application for decoding pathway enrichment analysis

The past decades have brought a steady growth of pathway databases and enrichment methods. However, the advent of pathway data has not been accompanied by an improvement in interoperability across databases, hampering the use of pathway knowledge from multiple databases for enrichment analysis. While integrative databases have attempted to address this issue, they often do not account for redundant information across resources. Furthermore, the majority of studies that employ pathway enrichment analysis still rely upon a single database or enrichment method, though the use of another could yield differing results. These shortcomings call for approaches that investigate the differences and agreements across databases and methods as their selection in the design of a pathway analysis can be a crucial step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run enrichment analysis or directly upload results and facilitate the interpretation of results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at https://decopath.scai.fraunhofer.de, and its source code and documentation can be found on GitHub at https://github.com/DecoPath/DecoPath.

Exploiting genomic synteny in Felidae: cross-species genome alignments and SNV discovery can aid conservation management

Background

While recent advances in genomics has enabled vast improvements in the quantification of genome-wide diversity and the identification of adaptive and deleterious alleles in model species, wildlife and non-model species have largely not reaped the same benefits. This has been attributed to the resources and infrastructure required to develop essential genomic datasets such as reference genomes. In the absence of a high-quality reference genome, cross-species alignments can provide reliable, cost-effective methods for single nucleotide variant (SNV) discovery. Here, we demonstrated the utility of cross-species genome alignment methods in gaining insights into population structure and functional genomic features in cheetah (Acinonyx jubatas), snow leopard (Panthera uncia) and Sumatran tiger (Panthera tigris sumatrae), relative to the domestic cat (Felis catus).

Results

Alignment of big cats to the domestic cat reference assembly yielded nearly complete sequence coverage of the reference genome. From this, 38,839,061 variants in cheetah, 15,504,143 in snow leopard and 13,414,953 in Sumatran tiger were discovered and annotated. This method was able to delineate population structure but limited in its ability to adequately detect rare variants. Enrichment analysis of fixed and species-specific SNVs revealed insights into adaptive traits, evolutionary history and the pathogenesis of heritable diseases.

Conclusions

The high degree of synteny among felid genomes enabled the successful application of the domestic cat reference in high-quality SNV detection. The datasets presented here provide a useful resource for future studies into population dynamics, evolutionary history and genetic and disease management of big cats. This cross-species method of variant discovery provides genomic context for identifying annotated gene regions essential to understanding adaptive and deleterious variants that can improve conservation outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07899-2.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

CPA: a web-based platform for consensus pathway analysis and interactive visualization

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

Circulating miR-455-3p, miR-5787, and miR-548a-3p as potential noninvasive biomarkers in the diagnosis of acute graft-versus-host disease: a validation study

Currently, acute graft-versus-host disease (aGVHD) diagnosis is based on clinical features and pathological findings. Until now, there is no non-invasive diagnostic test for aGVHD. MicroRNAs may act as promising predictive, diagnostic, or prognostic biomarkers for aGVHD. The purpose of the current study was to validate circulating microRNAs as diagnostic biomarkers to assist clinicians in promptly diagnosing aGVHD, so that treatment can be initiated earlier. In the present study, we evaluated six microRNAs (miR-455-3p, miR-5787, miR-6729-5p, miR-6776-5p, miR-548a-3p, and miR-6732-5p) selected from miRNA array data in 40 aGVHD patients compared to 40 non-GVHD patients with RT-qPCR. Target genes of differentially expressed microRNAs (DEMs) were predicted using Targetscan, miRanda, miRDB, miRWalk, PICTAR5, miRmap, DIANA, and miRTarBase algorithms, and their functions were analyzed using EnrichNet, Metascape, and DIANA-miRPath databases. The expressions of plasma miR-455-3p and miR-5787 were significantly downregulated, whereas miR-548a-3p was significantly upregulated in aGVHD patients compared to non-GVHD patients. Moreover, DEMs showed potentially high diagnostic accuracy for aGVHD. In silico analysis of DEMs provided valuable information on the role of DEMs in GVHD, immune regulation, and inflammatory response. Our study suggested that miR-455-3p, miR-5787, and miR-548a-3p could be used as potential noninvasive biomarkers in the diagnosis of aGVHD in addition to possible therapeutic targets in aGVHD.

Expression profiling of cultured podocytes exposed to nephrotic plasma reveals intrinsic molecular signatures of nephrotic syndrome

BACKGROUND

Nephrotic syndrome (NS) is a common renal disorder in children attributed to podocyte injury. However, children with the same diagnosis have markedly variable treatment responses, clinical courses, and outcomes, suggesting molecular heterogeneity.

PURPOSE

This study aimed to explore the molecular responses of podocytes to nephrotic plasma to identify specific genes and signaling pathways differentiating various clinical NS groups as well as biological processes that drive injury in normal podocytes.

METHODS

Transcriptome profiles from immortalized human podocyte cell line exposed to the plasma of 8 subjects (steroidsensitive nephrotic syndrome [SSNS], n=4; steroid-resistant nephrotic syndrome [SRNS], n=2; and healthy adult individuals [control], n=2) were generated using microarray analysis.

RESULTS

Unsupervised hierarchical clustering of global gene expression data was broadly correlated with the clinical classification of NS. Differential gene expression (DGE) analysis of diseased groups (SSNS or SRNS) versus healthy controls identified 105 genes (58 up-regulated, 47 down-regulated) in SSNS and 139 genes (78 up-regulated, 61 down-regulated) in SRNS with 55 common to SSNS and SRNS, while the rest were unique (50 in SSNS, 84 genes in SRNS). Pathway analysis of the significant (P≤0.05, -1≤ log2 FC ≥1) differentially expressed genes identified the transforming growth factor-β and Janus kinase-signal transducer and activator of transcription pathways to be involved in both SSNS and SRNS. DGE analysis of SSNS versus SRNS identified 2,350 genes with values of P≤0.05, and a heatmap of corresponding expression values of these genes in each subject showed clear differences in SSNS and SRNS.

CONCLUSION

Our study observations indicate that, although podocyte injury follows similar pathways in different clinical subgroups, the pathways are modulated differently as evidenced by the heatmap. Such transcriptome profiling with a larger cohort can stratify patients into intrinsic subtypes and provide insight into the molecular mechanisms of podocyte injury.

PathBIX-a web server for network-based pathway annotation with adaptive null models

Motivation

Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation.

Results

We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.

Availability

https://pathbix.sbc.su.se/.

Contact

erik.sonnhammer@scilifelab.se.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics

Background

Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance. However, a functional annotation may be dysregulated in a given experimental condition, while none, or very few of its proteins may be individually considered to be significantly differentially expressed. Such an annotation would therefore be missed by standard approaches.

Results

Herein, we propose a novel graph theory-based method, PIGNON, for the detection of differentially expressed functional annotations in different conditions. PIGNON does not assess the statistical significance of the differential expression of individual proteins, but rather maps protein differential expression levels onto a protein–protein interaction network and measures the clustering of proteins from a given functional annotation within the network. This process allows the detection of functional annotations for which the proteins are differentially expressed and grouped in the network. A Monte-Carlo sampling approach is used to assess the clustering significance of proteins in an expression-weighted network. When applied to a quantitative proteomics analysis of different molecular subtypes of breast cancer, PIGNON detects Gene Ontology terms that are both significantly clustered in a protein–protein interaction network and differentially expressed across different breast cancer subtypes. PIGNON identified functional annotations that are dysregulated and clustered within the network between the HER2+, triple negative and hormone receptor positive subtypes. We show that PIGNON’s results are complementary to those of state-of-the-art functional enrichment analyses and that it highlights functional annotations missed by standard approaches. Furthermore, PIGNON detects functional annotations that have been previously associated with specific breast cancer subtypes.

Conclusion

PIGNON provides an alternative to functional enrichment analyses and a more comprehensive characterization of quantitative datasets. Hence, it contributes to yielding a better understanding of dysregulated functions and processes in biological samples under different experimental conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04042-6.

Immunomodulatory Effects of Vitamin D Supplementation in a Deficient Population

In addition to its canonical functions, vitamin D has been proposed to be an important mediator of the immune system. Despite ample sunshine, vitamin D deficiency is prevalent (>80%) in the Middle East, resulting in a high rate of supplementation. However, the underlying molecular mechanisms of the specific regimen prescribed and the potential factors affecting an individual's response to vitamin D supplementation are not well characterized. Our objective is to describe the changes in the blood transcriptome and explore the potential mechanisms associated with vitamin D3 supplementation in one hundred vitamin D-deficient women who were given a weekly oral dose (50,000 IU) of vitamin D3 for three months. A high-throughput targeted PCR, composed of 264 genes representing the important blood transcriptomic fingerprints of health and disease states, was performed on pre and post-supplementation blood samples to profile the molecular response to vitamin D3. We identified 54 differentially expressed genes that were strongly modulated by vitamin D3 supplementation. Network analyses showed significant changes in the immune-related pathways such as TLR4/CD14 and IFN receptors, and catabolic processes related to NF-kB, which were subsequently confirmed by gene ontology enrichment analyses. We proposed a model for vitamin D3 response based on the expression changes of molecules involved in the receptor-mediated intra-cellular signaling pathways and the ensuing predicted effects on cytokine production. Overall, vitamin D3 has a strong effect on the immune system, G-coupled protein receptor signaling, and the ubiquitin system. We highlighted the major molecular changes and biological processes induced by vitamin D3, which will help to further investigate the effectiveness of vitamin D3 supplementation among individuals in the Middle East as well as other regions.

Popularity and performance of bioinformatics software: the case of gene set analysis

Background

Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.

Results

Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.

Conclusions

The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04124-5.

Computational study of zebrafish immune-targeted microarray data for prediction of preventive drug candidates

Viral hemorrhagic septicemia virus (VHSV) is a rhabdovirus reported to cause economic loss in fish farms. Because of the lack of adequate preventative treatments, the identification of multipath genes involved in VHS infection might be an alternative to explore the possibility of using drugs for the seasonal prevention of this fish disease. We propose labeling a category of drug molecules by further classification and interpretation of the Drug Gene Interaction Database using gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment scores. The study investigated disease networks of up-and down-regulated genes to find those with high interaction as substantial genes in pathways among the different disease networks. We prioritized these genes based on their relationship to those associated with VHS infection in the context of human protein-protein interaction networks and disease pathways. Among the 29 genes as potential drug targets, nine were selected as promising druggable genes (ERBB2, FGFR3, ITGA2B, MAP2K1, NGF, NTRK1, PDGFRA, SCN2B, and SERPINC1). PDGFRA is the most important druggable up-and down-regulated gene and is considered an important gene in the IMATINIB pathway. This study findings indicate a promising approach for drug target prediction for VHS treatment, which might be useful for disease therapeutics.

AM, Mahboob S. In-silico network-based analysis of drugs used against COVID-19: Human well-being study

INTRODUCTION

Researchers worldwide with great endeavor searching and repurpose drugs might be potentially useful in fighting newly emerged coronavirus. These drugs show inhibition but also show side effects and complications too. On December 27, 2020, 80,926,235 cases have been reported worldwide. Specifically, in Pakistan, 471,335 has been reported with inconsiderable deaths.

PROBLEM STATEMENT

Identification of COVID-19 drugs pathway through drug-gene and gene-gene interaction to find out the most important genes involved in the pathway to deal with the actual cause of side effects beyond the beneficent effects of the drugs.

METHODOLOGY

The medicines used to treat COVID-19 are retrieved from the Drug Bank. The drug-gene interaction was performed using the Drug Gene Interaction Database to check the relation between the genes and the drugs. The networks of genes are developed by Gene MANIA, while Cytoscape is used to check the active functional association of the targeted gene. The developed systems cross-validated using the EnrichNet tool and identify drug genes' concerned pathways using Reactome and STRING.

RESULTS

Five drugs Azithromycin, Bevacizumab, CQ, HCQ, and Lopinavir, are retrieved. The drug-gene interaction shows several genes that are targeted by the drug. Gene MANIA interaction network shows the functional association of the genes like co-expression, physical interaction, predicted, genetic interaction, co-localization, and shared protein domains.

CONCLUSION

Our study suggests the pathways for each drug in which targeted genes and medicines play a crucial role, which will help experts in-vitro overcome and deal with the side effects of these drugs, as we find out the in-silico gene analysis for the COVID-19 drugs.

Identification of dysregulated pathways through <i>SLC30A8</i> protein interaction in type 1 diabetes mellitus

<abstract><sec> <title>Objective</title> <p>The aim of the current study was to explore the gene enrichment and dysregulated pathways on the basis of interaction network analysis of <italic>SLC30A8</italic> in type 1 diabetes mellitus (T1DM). <italic>SLC30A8</italic> polymorphism could be characterized as a beneficial tool to identify the interacting gene in developing T1DM.</p> </sec><sec> <title>Materials and methods</title> <p><italic>SLC30A8</italic> interacting protein interaction network was obtained by String Interaction network Version 11.0. Ten proteins were identified interacting with <italic>SLC30A8</italic> and were analysed by protein-protein interaction and enrichment network analysis along with Functional Enrichment analysis tool (FunRich 3.1.3) to map the gene data sets. In entire analysis, FunRich database was used as background against all annotated gene/protein list. Protein-protein interaction (PPI) and enrichment network analysis of the selected protein: <italic>SLC30A8</italic> gene along with gene mapping and pathway enrichment were performed using FunRich 3.1.3 and String Interaction network Version 11.0.</p> </sec><sec> <title>Results</title> <p>Biological pathway grouping displayed enriched proteins in TRAIL signalling pathway (<italic>p</italic> &lt; 0.001). <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> were enriched in TRAIL Signalling pathway when <italic>INS</italic> was made focused gene and directly interacting with <italic>SLC30A8</italic>.</p> </sec><sec> <title>Conclusions</title> <p>TRAIL signalling pathways were enriched in T1DM. Therefore, <italic>SLC30A8</italic> along with <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> involved in TRAIL pathway must be further explored to understand their in vivo role in T1DM.</p> </sec></abstract>

Comprehensive pathway enrichment analysis workflows: COVID-19 case study

The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.

PathWalks: identifying pathway communities using a disease-related map of integrated information

MOTIVATION

Understanding the underlying biological mechanisms and respective interactions of a disease remains an elusive, time consuming and costly task. Computational methodologies that propose pathway/mechanism communities and reveal respective relationships can be of great value as they can help expedite the process of identifying how perturbations in a single pathway can affect other pathways.

RESULTS

We present a random-walks-based methodology called PathWalks, where a walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study.We apply the PathWalks methodology on Alzheimer's disease and idiopathic pulmonary fibrosis and establish that it can highlight pathways that are also identified by other pathway analysis tools as well as are backed through bibliographic references. More importantly, PathWalks produces additional new pathways that are functionally connected with those already established, giving insight for further experimentation.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vagkaratzas/PathWalks.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.