Lysines and cysteines: partners in stress?

Modifications of cysteine residues in redox-sensitive proteins are key to redox signaling and stress response in all organisms. A novel type of redox switch was recently discovered that comprises lysine and cysteine residues covalently linked by an nitrogen-oxygen-sulfur (NOS) bridge. Here, we discuss chemical and biological implications of this discovery.

HTRA1 Regulates Subclinical Inflammation and Activates Proangiogenic Response in the Retina and Choroid.

High-temperature requirement A1 (HtrA1) has been identified as a disease-susceptibility gene for age-related macular degeneration (AMD) including polypoidal choroidal neovasculopathy (PCV). We characterized the underlying phenotypic changes of transgenic (Tg) mice expressing ubiquitous CAG promoter (CAG-HtrA1 Tg). In vivo imaging modalities and histopathology were performed to investigate the possible neovascularization, drusen formation, and infiltration of macrophages. Subretinal white material deposition and scattered white-yellowish retinal foci were detected on CFP [(Tg­33% (20/60) and wild-type (WT)­7% (1/15), p < 0.05]. In 40% (4/10) of the CAG-HtrA1 Tg retina, ICGA showed punctate hyperfluorescent spots. There was no leakage on FFA and OCTA failed to confirm vascular flow signals from the subretinal materials. Increased macrophages and RPE cell migrations were noted from histopathological sections. Monocyte subpopulations were increased in peripheral blood in the CAG-HtrA1 Tg mice (p < 0.05). Laser induced CNV in the CAG-HtrA1 Tg mice and showed increased leakage from CNV compared to WT mice (p < 0.05). Finally, choroidal explants of the old CAG-HtrA1 Tg mice demonstrated an increased area of sprouting (p < 0.05). Signs of subclinical inflammation was observed in CAG-HtrA1 Tg mice. Such subclinical inflammation may have resulted in increased RPE cell activation and angiogenic potential.

GraphOmics: an interactive platform to explore and integrate multi-omics data.

BACKGROUND: An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. RESULTS: Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. CONCLUSIONS: GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/ . It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

Blue Biotechnology: Computational Screening of Sarcophyton Cembranoid Diterpenes for SARS-CoV-2 Main Protease Inhibition.

The coronavirus pandemic has affected more than 150 million people, while over 3.25 million people have died from the coronavirus disease 2019 (COVID-19). As there are no established therapies for COVID-19 treatment, drugs that inhibit viral replication are a promising target; specifically, the main protease (Mpro) that process CoV-encoded polyproteins serves as an Achilles heel for assembly of replication-transcription machinery as well as down-stream viral replication. In the search for potential antiviral drugs that target Mpro, a series of cembranoid diterpenes from the biologically active soft-coral genus Sarcophyton have been examined as SARS-CoV-2 Mpro inhibitors. Over 360 metabolites from the genus were screened using molecular docking calculations. Promising diterpenes were further characterized by molecular dynamics (MD) simulations based on molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. According to in silico calculations, five cembranoid diterpenes manifested adequate binding affinities as Mpro inhibitors with ΔGbinding < -33.0 kcal/mol. Binding energy and structural analyses of the most potent Sarcophyton inhibitor, bislatumlide A (340), was compared to darunavir, an HIV protease inhibitor that has been recently subjected to clinical-trial as an anti-COVID-19 drug. In silico analysis indicates that 340 has a higher binding affinity against Mpro than darunavir with ΔGbinding values of -43.8 and -34.8 kcal/mol, respectively throughout 100 ns MD simulations. Drug-likeness calculations revealed robust bioavailability and protein-protein interactions were identified for 340; biochemical signaling genes included ACE, MAPK14 and ESR1 as identified based on a STRING database. Pathway enrichment analysis combined with reactome mining revealed that 340 has the capability to re-modulate the p38 MAPK pathway hijacked by SARS-CoV-2 and antagonize injurious effects. These findings justify further in vivo and in vitro testing of 340 as an antiviral agent against SARS-CoV-2.

Expansion of Single Cell Transcriptomics Data of SARS-CoV Infection in Human Bronchial Epithelial Cells to COVID-19.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 19 (COVID-19) that was emerged as a new member of coronaviruses since December 2019 in Wuhan, China and then after was spread in all continentals. Since SARS-CoV-2 has shown about 77.5% similarity to SARS-CoV, the transcriptome and immunological regulations of SARS-CoV-2 was expected to have high percentage of overlap with SARS-CoV. RESULTS: In this study, we applied the single cell transcriptomics data of human bronchial epithelial cells (2B4 cell line) infected with SARS-CoV, which was annotated in the Expression Atlas database to expand this data to COVID-19. In addition, we employed system biology methods including gene ontology (GO) and Reactome pathway analyses to define functional genes and pathways in the infected cells with SARS-CoV. The transcriptomics analysis on the Expression Atlas database revealed that most genes from infected 2B4 cell line with SARS-CoV were downregulated leading to immune system hyperactivation, induction of signaling pathways, and consequently a cytokine storm. In addition, GO:0016192 (vesicle-mediated transport), GO:0006886 (intracellular protein transport), and GO:0006888 (ER to Golgi vesicle-mediated transport) were shown as top three GOs in the ontology network of infected cells with SARS-CoV. Meanwhile, R-HAS-6807070 (phosphatase and tensin homolog or PTEN regulation) showed the highest association with other Reactome pathways in the network of infected cells with SARS-CoV. PTEN plays a critical role in the activation of dendritic cells, B- and T-cells, and secretion of proinflammatory cytokines, which cooperates with downregulated genes in the promotion of cytokine storm in the COVID-19 patients. CONCLUSIONS: Based on the high similarity percentage of the transcriptome of SARS-CoV with SARS-CoV-2, the data of immunological regulations, signaling pathways, and proinflammatory cytokines in SARS-CoV infection can be expanded to COVID-19 to have a valid platform for future pharmaceutical and vaccine studies.

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high. RESULTS: We developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne . CONCLUSION: FunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.

Interleukins and their signaling pathways in the Reactome biological pathway database.

BACKGROUND: There is a wealth of biological pathway information available in the scientific literature, but it is spread across many thousands of publications. Alongside publications that contain definitive experimental discoveries are many others that have been dismissed as spurious, found to be irreproducible, or are contradicted by later results and consequently now considered controversial. Many descriptions and images of pathways are incomplete stylized representations that assume the reader is an expert and familiar with the established details of the process, which are consequently not fully explained. Pathway representations in publications frequently do not represent a complete, detailed, and unambiguous description of the molecules involved; their precise posttranslational state; or a full account of the molecular events they undergo while participating in a process. Although this might be sufficient to be interpreted by an expert reader, the lack of detail makes such pathways less useful and difficult to understand for anyone unfamiliar with the area and of limited use as the basis for computational models. OBJECTIVE: Reactome was established as a freely accessible knowledge base of human biological pathways. It is manually populated with interconnected molecular events that fully detail the molecular participants linked to published experimental data and background material by using a formal and open data structure that facilitates computational reuse. These data are accessible on a Web site in the form of pathway diagrams that have descriptive summaries and annotations and as downloadable data sets in several formats that can be reused with other computational tools. The entire database and all supporting software can be downloaded and reused under a Creative Commons license. METHODS: Pathways are authored by expert biologists who work with Reactome curators and editorial staff to represent the consensus in the field. Pathways are represented as interactive diagrams that include as much molecular detail as possible and are linked to literature citations that contain supporting experimental details. All newly created events undergo a peer-review process before they are added to the database and made available on the associated Web site. New content is added quarterly. RESULTS: The 63rd release of Reactome in December 2017 contains 10,996 human proteins participating in 11,426 events in 2,179 pathways. In addition, analytic tools allow data set submission for the identification and visualization of pathway enrichment and representation of expression profiles as an overlay on Reactome pathways. Protein-protein and compound-protein interactions from several sources, including custom user data sets, can be added to extend pathways. Pathway diagrams and analytic result displays can be downloaded as editable images, human-readable reports, and files in several standard formats that are suitable for computational reuse. Reactome content is available programmatically through a REpresentational State Transfer (REST)-based content service and as a Neo4J graph database. Signaling pathways for IL-1 to IL-38 are hierarchically classified within the pathway "signaling by interleukins." The classification used is largely derived from Akdis et al. CONCLUSION: The addition to Reactome of a complete set of the known human interleukins, their receptors, and established signaling pathways linked to annotations of relevant aspects of immune function provides a significant computationally accessible resource of information about this important family. This information can be extended easily as new discoveries become accepted as the consensus in the field. A key aim for the future is to increase coverage of gene expression changes induced by interleukin signaling.

Functional and Structural Features of Disease-Related Protein Variants.

Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations.

Decreased E-Cadherin in MCF7 Human Breast Cancer Cells Forming Multicellular Spheroids Exposed to Simulated Microgravity.

MCF7 human breast cancer cells were cultured under normal gravity (1 g) and on a random positioning machine (RPM) preventing sedimentation. After 2 weeks, adherent 1 g-control and adherent RPM cells (AD) as well as multicellular spheroids (MCS) were harvested. AD and MCS had been exposed to the RPM in the same culture flask. In a subsequent proteome analysis, the majority of the proteins detected showed similar label-free quantification (LFQ) scores in each of the respective subpopulations, but in both AD or MCS cultures, proteins were also found whose LFQs deviated at least twofold from their counterparts in the 1 g-control cells. They included the cell junction protein E-cadherin, which was diminished in MCS cells, where proteins of the E-cadherin autodegradation pathway were enhanced and c-Src (proto-oncogene tyrosine-protein kinase c-Src) was detected. Spheroid formation was prevented by inhibition of c-Src but promoted by antibodies blocking E-cadherin activity. An interaction analysis of the detected proteins that are involved in forming and regulating junctions or adhesion complexes and in E-cadherin autodegradation indicated connections between the two protein groups. This suggests that the balance of proteins that up- or downregulate E-cadherin mediates the tendency of MCF7 cells to form MCS during RPM exposure.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.

BACKGROUND: Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. RESULTS: An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. CONCLUSIONS: We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

An attempt to understand glioma stem cell biology through centrality analysis of a protein interaction network.

Biological networks can be analyzed using "Centrality Analysis" to identify the more influential nodes and interactions in the network. This study was undertaken to create and visualize a biological network comprising of protein-protein interactions (PPIs) amongst proteins which are preferentially over-expressed in glioma cancer stem cell component (GCSC) of glioblastomas as compared to the glioma non-stem cancer cell (GNSC) component and then to analyze this network through centrality analyses (CA) in order to identify the essential proteins in this network and their interactions. In addition, this study proposes a new centrality analysis method pertaining exclusively to transcription factors (TFs) and interactions amongst them. Moreover the relevant molecular functions, biological processes and biochemical pathways amongst these proteins were sought through enrichment analysis. A protein interaction network was created using a list of proteins which have been shown to be preferentially expressed or over-expressed in GCSCs isolated from glioblastomas as compared to the GNSCs. This list comprising of 38 proteins, created using manual literature mining, was submitted to the Reactome FIViz tool, a web based application integrated into Cytoscape, an open source software platform for visualizing and analyzing molecular interaction networks and biological pathways to produce the network. This network was subjected to centrality analyses utilizing ranked lists of six centrality measures using the FIViz application and (for the first time) a dedicated centrality analysis plug-in ; CytoNCA. The interactions exclusively amongst the transcription factors were nalyzed through a newly proposed centrality analysis method called "Gene Expression Associated Degree Centrality Analysis (GEADCA)". Enrichment analysis was performed using the "network function analysis" tool on Reactome. The CA was able to identify a small set of proteins with consistently high centrality ranks that is indicative of their strong influence in the protein protein interaction network. Similarly the newly proposed GEADCA helped identify the transcription factors with high centrality values indicative of their key roles in transcriptional regulation. The enrichment studies provided a list of molecular functions, biological processes and biochemical pathways associated with the constructed network. The study shows how pathway based databases may be used to create and analyze a relevant protein interaction network in glioma cancer stem cells and identify the essential elements within it to gather insights into the molecular interactions that regulate the properties of glioma stem cells. How these insights may be utilized to help the development of future research towards formulation of new management strategies have been discussed from a theoretical standpoint.

Bioinformatic Analyses of Renal Ischaemia-Reperfusion Injury Models: Identification of Key Genes Involved in the Development of Kidney Disease.

BACKGROUND/AIMS: To develop a novel strategy for the treatment of kidney disease, we explored potential molecular targets involved in the development of renal ischaemia-reperfusion injury (IRI). METHODS: The Gene expression profile data of GSE27274, including controls and rats subjected to renal IRI and reperfusion for 24 h (IR24) or 120 h (IR120), was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analysed using the limma package. Gene Ontology (GO) and pathway functional enrichment analyses of common DEGs were carried out. Protein-protein interactions (PPI) and miRNA-DEG network analyses were performed using the STRING database and WebGestalt, respectively, followed by network construction using Cytoscape. RESULTS: In total, 80 common DEGs (41 up- and 39 downregulated genes) between IR24 and IR120 were screened. Genes encoding tissue inhibitor of matrix metalloproteinase-1 (Timp1), secreted phosphoprotein 1 (Spp1) and dimethylglycine dehydrogenase (Dmgdh) were identified as hub genes in the PPI network and may be significant in the development of renal IRI. Upregulated Spp1 was enriched in the inflammatory response, and downregulated Dmgdh was enriched in the catabolic process of the amino acid betaine. In reactome pathway analyses, Spp1 was enriched in toll-like receptor signalling, and Dmgdh was enriched in glycine, serine and threonine metabolic pathways. The common DEGs were mainly regulated by 15 miRNA clusters. CONCLUSION: Timp1, Spp1, Dmgdh, miR-142-5p and miR-181a may be potential targets or biomarkers for the development of renal IRI.

Functional proteomic analysis of crossbred (Holstein Friesian × Sahiwal) bull spermatozoa.

Male infertility is one of the prime concerns of dairy cattle production. The study was designed to find out differentially expressed proteins in categorized crossbred (Holstein Friesian × Sahiwal) bull semen to serve as potential biomarkers for male infertility. Frozen crossbred bull semen with satisfactory phenotypic records were defined as "good" and "poor" based on their fertility rates. A total of 1,547 proteins were detected in bull spermatozoa using liquid chromatography-mass spectrometer (LC-MS/MS) analysis. Results revealed that 558 (36.1%) and 653 (42.2%) proteins were expressed to good and poor quality bull spermatozoa, respectively. A total of 336 proteins (21.7%) were reported to be unique for both good and poor quality bull semen, and among the common proteins, 224 (66.7%) and 112 (33.3%) were up- and downregulated in good and poor quality categorized bull semen, respectively. Gene Ontology analysis of global proteomes identified different signalling pathways, and most of them were related to cellular motility, immune systems as well as cellular metabolisms. The distinctive presence of some of the proteins may provide an insight into the molecular mechanistic role played by these proteins in crossbred bull infertility.

Semantic Analysis of Posttranslational Modification of Proteins Accumulated in Thyroid Cancer Cells Exposed to Simulated Microgravity.

When monolayers of tissue cancer cells of various origins are exposed to real or simulated microgravity, many cells leave the monolayer and assemble to three-dimensional (3D) aggregates (spheroids). In order to define the cellular machinery leading to this change in growth behavior of FTC-133 human thyroid cancer cells and MCF-7 breast cancer cells, we recently performed proteome analyses on these cell lines and determined the proteins' accumulation in monolayer cells grown under 1g-conditions as well as in the cells of spheroids assembled under simulated microgravity during three and 14 days, respectively. At that time, an influence of the increment or decrement of some of the more than 5000 proteins detected in each cell line was investigated. In this study, we focused on posttranslational modifications (PTMs) of proteins. For this purpose, we selected candidates from the list of the proteins detected in the two preceding proteome analyses, which showed significant accumulation in spheroid cells as compared to 1g monolayer cells. Then we searched for those PTMs of the selected proteins, which according to the literature have already been determined experimentally. Using the Semantic Protocol and RDF Query Language (SPARQL), various databases were examined. Most efficient was the search in the latest version of the dbPTM database. In total, we found 72 different classes of PTMs comprising mainly phosphorylation, glycosylation, ubiquitination and acetylation. Most interestingly, in 35 of the 69 proteins, N6 residues of lysine are modifiable.

Microarray and network-based identification of functional modules and pathways of active tuberculosis.

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNet algorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB.

Using an in situ proximity ligation assay to systematically profile endogenous protein-protein interactions in a pathway network.

Signal transduction pathways in the cell require protein-protein interactions (PPIs) to respond to environmental cues. Diverse experimental techniques for detecting PPIs have been developed. However, the huge amount of PPI data accumulated from various sources poses a challenge with respect to data reliability. Herein, we collected ∼ 700 primary antibodies and employed a highly sensitive and specific technique, an in situ proximity ligation assay, to investigate 1204 endogenous PPIs in HeLa cells, and 557 PPIs of them tested positive. To overview the tested PPIs, we mapped them into 13 PPI public databases, which showed 72% of them were annotated in the Human Protein Reference Database (HPRD) and 8 PPIs were new PPIs not in the PubMed database. Moreover, TP53, CTNNB1, AKT1, CDKN1A, and CASP3 were the top 5 proteins prioritized by topology analyses of the 557 PPI network. Integration of the PPI-pathway interaction revealed that 90 PPIs were cross-talk PPIs linking 17 signaling pathways based on Reactome annotations. The top 2 connected cross-talk PPIs are MAPK3-DAPK1 and FAS-PRKCA interactions, which link 9 and 8 pathways, respectively. In summary, we established an open resource for biological modules and signaling pathway profiles, providing a foundation for comprehensive analysis of the human interactome.

Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data.

Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that exploiting RDF/OWL semantics while conducting ML could provide useful insights. We present a use case using signaling pathways from the Reactome database to explore drug safety. Promising outcomes suggest the need for further investigation and collaboration with domain experts.

Chemical coverage of human biological pathways.

Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology, and uncover novel targets for precision medicine. The mission of the Target 2035 initiative is to discover chemical tools for all human proteins by the year 2035. Here, we draw a landscape of the current chemical coverage of human biological pathways. Although available chemical tools target only 3% of the human proteome, they already cover 53% of human biological pathways and represent a versatile toolkit to dissect a vast portion of human biology. Pathways targeted by existing drugs may be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.

The digestion and dietary carbohydrate pathway contains 100% gene mutations enrichment among 117 patients with major depressive disorder.

Introduction: Major depressive disorder (MDD) is partially inheritable while its mechanism is still uncertain. Methods: This cross-sectional study focused on gene pathways as a whole rather than polymorphisms of single genes. Deep sequencing and gene enrichment analysis based on pathways in Reactome database were obtained to reveal gene mutations. Results: A total of 117 patients with MDD and 78 healthy controls were enrolled. The Digestion and Dietary Carbohydrate pathway (Carbohydrate pathway) was determined to contain 100% mutations in patients with MDD and 0 mutation in matched healthy controls. Discussion: Findings revealed in the current study enable a better understanding of gene pathways mutations status in MDD patients, indicating a possible genetic mechanism of MDD development and a potential diagnostic or therapeutic target.

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer.

INTRODUCTION: Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels. METHODS: In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression. RESULTS: Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways. CONCLUSIONS: This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.