Identification of active signaling pathways by integrating gene expression and protein interaction data

Signaling pathways of dental implants' osseointegration: a narrative review on two of the most relevant; NF-κB and Wnt pathways

INTRODUCTION

Cell signaling pathways are the biological reactions that control cell functions and fate. They also directly affect the body reactions to implanted biomaterials. It is well-known that dental implants success depends on a successful integration with the alveolar bone: "osseointegration" which events comprise early and later responses to the implanted biomaterials. The early events are mainly immune-inflammatory responses to the implant considered by its microenvironment as a foreign body. Later reactions are osteogenic aiming to regulate bone formation and remodeling. All these events are controlled by the cell signaling pathways in an incredible harmonious coordination.

AIM

The number of pathways having a role in osseointegration is so big to be reviewed in a single article. So the aim of this review was to study only two of the most relevant ones: the inflammatory Nuclear Factor Kappa B (NF-κB) pathway regulating the early osseointegration events and the osteogenic Wnt pathway regulating later events.

METHODS

We conducted a literature review using key databases to provide an overview about the NF-κB and Wnt cell signaling pathways and their mutual relationship with dental implants. A simplified narrative approach was conducted to explain these cell signaling pathways, their mode of activation and how they are related to the cellular events of osseointegration.

RESULTS AND CONCLUSION

NF-κB and Wnt cell signaling pathways are important cross-talking pathways that are affected by the implant's material and surface characteristics. The presence of the implant itself in the bone alters the intracellular events of both pathways in the adjacent implant's cellular microenvironment. Both pathways have a great role in the success or failure of osseointegration. Such knowledge can offer a new hope to treat failed implants and enhance osseointegration in difficult cases. This is consistent with advances in Omics technologies that can change the paradigm of dental implant therapy.

PARP-1 is a transcriptional rheostat of metabolic and bivalent genes during development

PARP-1 participates in various cellular processes, including gene regulation. In Drosophila, PARP-1 mutants undergo developmental arrest during larval-to-pupal transition. In this study, we investigated PARP-1 binding and its transcriptional regulatory role at this stage. Our findings revealed that PARP-1 binds and represses active metabolic genes, including glycolytic genes, whereas activating low-expression developmental genes, including a subset of "bivalent" genes in third-instar larvae. These bivalent promoters, characterized by dual enrichment of low H3K4me3 and high H3K27me3, a unimodal H3K4me1 enrichment at the transcription start site (conserved in C. elegans and zebrafish), H2Av depletion, and high accessibility, may persist throughout development. In PARP-1 mutant third-instar larvae, metabolic genes typically down-regulated during the larval-to-pupal transition in response to reduced energy needs were repressed by PARP-1. Simultaneously, developmental and bivalent genes typically active at this stage were activated by PARP-1. In addition, glucose and ATP levels were significantly reduced in PARP-1 mutants, suggesting an imbalance in metabolic regulation. We propose that PARP-1 is essential for maintaining the delicate balance between metabolic and developmental gene expression programs to ensure proper developmental progression.

Signaling network analysis reveals fostamatinib as a potential drug to control platelet hyperactivation during SARS-CoV-2 infection

Introduction

Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear.

Methods

We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved.

Results

Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients.

Discussion

Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.

Using epigenomics to understand cellular responses to environmental influences in diseases

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.

Transcriptomic Responses of Human Retinal Vascular Endothelial Cells to Inflammatory Cytokines

Purpose

Molecular profiling of human retinal endothelial cells provides opportunities to understand the roles of this cell population in maintenance of the blood-ocular barrier, and its involvements in diverse retinal vasculopathies. We aimed to generate a transcriptome of human retinal endothelial cells in the unstimulated state, and following treatment with inflammatory cytokines linked to cell dysfunction.

Methods

Endothelial cells were isolated from retinae of five human cadaveric donors, and treated for 60 minutes and 24 hours with interleukin-1β or tumor necrosis factor-α, or exposed to medium alone for the same intervals. Expression of intercellular adhesion molecule-1 was measured by RT-qPCR to confirm cytokine-induced activation of the cells. RNA was sequenced on the Illumina NovaSeq 6000 platform. Reads were aligned to the human GRCh38 genome, and reads that aligned to Ensembl-annotated genes were counted. Quality control of sequencing was performed with FastQC, and sequences were classified by Kraken.

Results

A human retinal endothelial cell RNA-sequencing dataset with mean of 99% reads aligned to the human genome was produced as raw RNA sequence data (FASTQ files) and processed read data (XLSX files). Multidimensional scaling analysis showed a strong donor effect, which was readily controlled by ComBat.

Conclusions

Our dataset may be useful for human retinal endothelial cell transcriptomic assemblies, functional gene annotating and/or gene expression and enrichment analyses, as well as cross-dataset harmonization.

Translational Relevance

The molecular profile of the human retinal endothelium is a source of candidate biologic targets for retinal vasculopathies.

Single-cell RNA sequencing predicts motility networks in purified human gastric interstitial cells of Cajal

BACKGROUND

Gastrointestinal (GI) motility disorders affect millions of people worldwide, yet they remain poorly treated in part due to insufficient knowledge of the molecular networks controlling GI motility. Interstitial cells of Cajal (ICC) are critical GI pacemaker cells, and abnormalities in ICC are implicated in GI motility disorders. Two cell surface proteins, KIT and ANO1, are used for identifying ICC. However, difficulties accessing human tissue and the low frequency of ICC in GI tissues have meant human ICC are insufficiently characterized. Here, a range of characterization assays including single-cell RNA sequencing (scRNA-seq) was performed using KIT⁺ CD45^- CD11B^- primary human gastric ICC to better understand networks controlling human ICC biology.

METHODS

Excess sleeve gastrectomy tissues were dissected; ICC were analyzed by immunofluorescence, fluorescence-activated cell sorting (FACSorting), real-time PCR, mass spectrometry, and scRNA-seq.

KEY RESULTS

Immunofluorescence identified ANO1⁺ /KIT⁺ cells throughout the gastric muscle. Compared to the FACSorted negative cells, PCR showed the KIT⁺ CD45^- CD11B^- ICC were enriched 28-fold in ANO1 expression (p < 0.01). scRNA-seq analysis of the KIT^- CD45⁺ CD11B⁺ and KIT⁺ CD45^- CD11B^- ICC revealed separate clusters of immune cells and ICC (respectively); cells in the ICC cluster expressed critical GI motility genes (eg, CAV1 and PRKG1). The scRNA-seq data for these two cell clusters predicted protein interaction networks consistent with immune cell and ICC biology, respectively.

CONCLUSIONS & INFERENCES

The single-cell transcriptome of purified KIT⁺ CD45^- CD11B^- human gastric ICC presented here provides new molecular insights and hypotheses into evolving models of GI motility. This knowledge will provide an improved framework to investigate targeted therapies for GI motility disorders.

Signaling Pathways Involved in Myocardial Ischemia-Reperfusion Injury and Cardioprotection: A Systematic Review of Transcriptomic Studies in Sus scrofa

Myocardial damage in acute myocardial infarctions (AMI) is primarily the result of ischemia−reperfusion injury (IRI). Recognizing the timing of transcriptional events and their modulation by cardioprotective strategies is critical to address the pathophysiology of myocardial IRI. Despite the relevance of pigs for translational studies of AMI, only a few have identified how transcriptomic changes shape cellular signaling pathways in response to injury. We systematically reviewed transcriptomic studies of myocardial IRI and cardioprotection in Sus scrofa. Gene expression datasets were analyzed for significantly enriched terms using the Enrichr analysis tool, and statistically significant results (adjusted p-values of <0.05) for Signaling Pathways, Transcription Factors, Molecular Functions, and Biological Processes were compared between eligible studies to describe how these dynamic changes transform the myocardium from an injured and inflamed tissue into a scar. Then, we address how cardioprotective interventions distinctly modulate the myocardial transcriptome and discuss the implications of uncovering gene regulatory networks for cardiovascular pathologies and translational applications.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Efficient induction and sustenance of pluripotent stem cells from bovine somatic cells

Although derivation of naïve bovine embryonic stem cells is unachieved, the possibility for generation of bovine induced pluripotent stem cells (biPSCs) has been generally reported. However, attempts to sustain biPSCs by promoting self-renewal have not been successful. Methods established for maintaining murine and human induced pluripotent stem cells (iPSCs) do not support self-renewal of iPSCs for any bovid species. In this study, we examined methods to enhance complete reprogramming and concurrently investigated signaling relevant to pluripotency of the bovine blastocyst inner cell mass (ICM). First, we identified that forced expression of SV40 large T antigen together with the reprogramming genes (OCT4, SOX2, KLF4 and MYC) substantially enhanced the reprogramming efficacy of bovine fibroblasts to biPSCs. Second, we uncovered that TGFβ signaling is actively perturbed in the ICM. Inhibition of ALK4/5/7 to block TGFβ/activin/nodal signaling together with GSK3β and MEK1/2 supported robust in vitro self-renewal of naïve biPSCs with unvarying colony morphology, steady expansion, expected pluripotency gene expression and committed differentiation plasticity. Core similarities between biPSCs and stem cells of the 16-cell-stage bovine embryo indicated a stable ground state of pluripotency; this allowed us to reliably gain predictive understanding of signaling in bovine pluripotency using systems biology approaches. Beyond defining a high-fidelity platform for advancing biPSC-based biotechnologies that have not been previously practicable, these findings also represent a significant step towards understanding corollaries and divergent aspects of bovine pluripotency.

This article has an associated First Person interview with the joint first authors of the paper.

Summary: Pluripotency reprogramming by overcoming the stable epigenome of bovine cells, and uncovering precise early embryo self-renewal mechanisms enables sustenance and expansion of authentic induced pluripotent stem cells in vitro.

Nanotechnology Approaches for Enhanced CNS Drug Delivery in the Management of Schizophrenia

Schizophrenia is a neuropsychiatric disorder mainly affecting the central nervous system, presented with auditory and visual hallucinations, delusion and withdrawal from society. Abnormal dopamine levels mainly characterise the disease; various theories of neurotransmitters explain the pathophysiology of the disease. The current therapeutic approach deals with the systemic administration of drugs other than the enteral route, altering the neurotransmitter levels within the brain and providing symptomatic relief. Fluid biomarkers help in the early detection of the disease, which would improve the therapeutic efficacy. However, the major challenge faced in CNS drug delivery is the blood-brain barrier. Nanotherapeutic approaches may overcome these limitations, which will improve safety, efficacy, and targeted drug delivery. This review article addresses the main challenges faced in CNS drug delivery and the significance of current therapeutic strategies and nanotherapeutic approaches for a better understanding and enhanced drug delivery to the brain, which improve the quality of life of schizophrenia patients.

Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.

Genomic signatures of the unjamming transition in compressed human bronchial epithelial cells

Epithelial tissue can transition from a jammed, solid-like, quiescent phase to an unjammed, fluid-like, migratory phase, but the underlying molecular events of the unjamming transition (UJT) remain largely unexplored. Using primary human bronchial epithelial cells (HBECs) and one well-defined trigger of the UJT, compression mimicking the mechanical effects of bronchoconstriction, here, we combine RNA sequencing data with protein-protein interaction networks to provide the first genome-wide analysis of the UJT. Our results show that compression induces an early transcriptional activation of the membrane and actomyosin network and a delayed activation of the extracellular matrix (ECM) and cell-matrix networks. This response is associated with a signaling cascade that promotes actin polymerization and cellular motility through the coordinated interplay of downstream pathways including ERK, JNK, integrin signaling, and energy metabolism. Moreover, in nonasthmatic versus asthmatic HBECs, common genomic patterns associated with ECM remodeling suggest a molecular connection between airway remodeling, bronchoconstriction, and the UJT.

Vertical integration methods for gene expression data analysis

Gene expression data have played an essential role in many biomedical studies. When the number of genes is large and sample size is limited, there is a 'lack of information' problem, leading to low-quality findings. To tackle this problem, both horizontal and vertical data integrations have been developed, where vertical integration methods collectively analyze data on gene expressions as well as their regulators (such as mutations, DNA methylation and miRNAs). In this article, we conduct a selective review of vertical data integration methods for gene expression data. The reviewed methods cover both marginal and joint analysis and supervised and unsupervised analysis. The main goal is to provide a sketch of the vertical data integration paradigm without digging into too many technical details. We also briefly discuss potential pitfalls, directions for future developments and application notes.

Single cell RNA-sequencing data generated from human pluripotent stem cell-derived lens epithelial cells

Detailed transcriptomic analyses of differentiated cell populations derived from human pluripotent stem cells is routinely used to assess the identity and utility of the differentiated cells. Here we provide single cell RNA-sequencing data obtained from ROR1-expressing lens epithelial cells (ROR1e LECs), obtained via directed differentiation of CA1 human embryonic stem cells. Analysis of the data using principal component analysis, heat maps and gene ontology assessments revealed phenotypes associated with lens epithelial cells. These data provide a resource for future characterisation of both normal and cataractous human lens biology. Corresponding morphological and functional data obtained from ROR1e LECs are reported in the associated research article "A simplified method for producing human lens epithelial cells and light-focusing micro-lenses from pluripotent stem cells " (Dewi et al., 2020).

Application of the RBBP9 Serine Hydrolase Inhibitor, ML114, Decouples Human Pluripotent Stem Cell Proliferation and Differentiation

Retinoblastoma binding protein 9 (RBBP9) is required for maintaining the expression of both pluripotency and cell cycle genes in human pluripotent stem cells (hPSCs). An siRNA-based study from our group showed it does so by influencing cell cycle progression through the RB/E2F pathway. In non-pluripotent cells, RBBP9 is also known to have serine hydrolase (SH) activity, acting on currently undefined target proteins. The role of RBBP9 SH activity in hPSCs, and during normal development, is currently unknown. To begin assessing whether RBBP9 SH activity might contribute to hPSC maintenance, hPSCs were treated with ML114—a selective chemical inhibitor of RBBP9 SH activity. Stem cells treated with ML114 showed significantly reduced population growth rate, colony size and progression through the cell cycle, with no observable change in cell morphology or decrease in pluripotency antigen expression—suggesting no initiation of hPSC differentiation. Consistent with this, hPSCs treated with ML114 retained the capacity for tri-lineage differentiation, as seen through teratoma formation. Subsequent microarray and Western blot analyses of ML114-treated hPSCs suggest the nuclear transcription factor Y subunit A (NFYA) may be a candidate effector of RBBP9 SH activity in hPSCs. These data support a role for RBBP9 in regulating hPSC proliferation independent of differentiation, whereby inhibition of RBBP9 SH activity de-couples decreased hPSC proliferation from initiation of differentiation.

Crosstalk between MEIS1 and markers of different cell signaling pathways in esophageal squamous cell carcinoma

The homeobox transcription factor MEIS1 is involved in cell fate decision, stem cells properties, gastrointestinal (GI) tract development, and progression of several malignancies such as esophageal squamous cell carcinoma (ESCC). Increasing evidences suggest the crosstalk between MEIS1 and cell signaling pathways. Therefore, our aim in present study was to investigate the probable linkage of MEIS1 expression with key genes of different cell signaling pathways in ESCC tumorigenesis, and their correlation with clinicopathological feature of the patients. The gene expression profiling of MEIS1 and different cell signaling genes including SALL4, SIZN1, and HEY1 (stemness state, BMP, and NOTCH signaling pathways, respectively) was performed using quantitative real-time reverse transcription polymerase chain reaction (PCR) in fresh tumoral compared to margin normal tissues of 50 treatment-naive ESCC samples. The mRNA expression of MEIS1/SIZN1, SIZN1/HEY1, and SIZN1/SALL4 were significantly associated to each other (P < 0.05). There were remarkable correlations between concomitant mRNA expression of MEIS1 and SIZN1 in tumors with invasion to adventitia, early stages of tumor progression and poorly differentiated tumors. Moreover, expression of MEIS1 and HEY1 was correlated to each other in primary stages of tumor progression and non-invaded tumors. Expression of MEIS1 was significantly associated with SALL4 in poorly differentiated tumors. Our results indicated that correlation between different cell signaling pathway-related genes may lead to esophageal tumorigenesis. It is illustrated that MEIS1 as a HOX gene has a significant correlation with stemness state, BMP, and NOTCH signaling pathways via the SIZN1.

LncRNA ROR1‑AS1 high expression and its prognostic significance in liver cancer

Hepatocellular carcinoma (HCC) is a common disease of the digestive system with no curative treatments. Long noncoding RNA tyrosine protein kinase transmembrane receptor 1 antisense RNA 1 (lncRNA ROR1-AS1) is an lncRNA whose functions have been predicted in human diseases; however, its important role in cancer has been probed only in mantle cell lymphoma, not in HCC. Therefore, the present study aimed to elucidate the prognostic significance of lncRNA ROR1-AS1 in HCC. The Cancer Genome Atlas Liver Hepatocellular Carcinoma was used to analyze the expression of ROR1-AS1 in liver cancer. χ² tests were performed to evaluate associations between clinical characteristics and ROR1-AS1 expression. The role of ROR1-AS1 in HCC prognosis was assessed using Kaplan-Meier curves and proportional hazards model (Cox) analysis. Gene set enrichment analysis was performed by using a Gene Expression Omnibus dataset. At the same time, Multi Experiment Matrix was used to predict genes that may be co-expressed with ROR1-AS1. The Database for Annotation, Visualization and Integrated Discovery and KO-Based Annotation System were used to analyze the most closely associated cytological behaviors and pathways in HCC. Then, the genes in the three databases were integrated to screen mRNAs, microRNAs and lncRNAs that had co-expression relationships with ROR1-AS1. Cytoscape, Search Tool for the Retrieval of Interacting Genes/Proteins and Molecular Evolutionary Genetics Analysis were used to map potential regulatory networks and developmental relationships associated with ROR1-AS1. Finally, 12 genes most closely associated with ROR1-AS1 were identified, and their relationship was described using a Circos plot. The results showed that ROR1-AS1 was upregulated in HCC, and its expression was related to clinical stage, T stage and N stage. Furthermore, Kaplan-Meier curves and Cox analysis indicated that high expression of ROR1-AS1 was associated with poor prognosis, and that ROR1-AS1 was an independent risk factor for HCC. Co-expression data suggested that there may be a large regulatory network of 45 genes with indirect associations with ROR1-AS1, a small regulatory network of 15 genes with direct or indirect regulatory relationships, and a special regulatory network containing 12 genes directly associated with ROR1-AS1. The present findings indicated that high expression of ROR1-AS1 suggests poor prognosis in patients with HCC.

Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

Identification of new drug and cell therapy targets for disease treatment will be facilitated by a detailed molecular understanding of normal and disease development. Human pluripotent stem cells can provide a large in vitro source of human cell types and, in a growing number of instances, also three-dimensional multicellular tissues called organoids. The application of stem cell technology to discovery and development of new therapies will be aided by detailed molecular characterisation of cell identity, cell signalling pathways and target gene networks. Big data or 'omics' techniques-particularly transcriptomics and proteomics-facilitate cell and tissue characterisation using thousands to tens-of-thousands of genes or proteins. These gene and protein profiles are analysed using existing and/or emergent bioinformatics methods, including a growing number of methods that compare sample profiles against compendia of reference samples. This review assesses how compendium-based analyses can aid the application of stem cell technology for new therapy development. This includes via robust definition of differentiated stem cell identity, as well as elucidation of complex signalling pathways and target gene networks involved in normal and diseased states.