Gene expression profiling meta-analysis reveals novel gene signatures and pathways shared between tuberculosis and rheumatoid arthritis

PIK3IP1: structure, aberration, function, and regulation in diseases

Phosphoinositide 3-kinase (PI3K) pathway, controlling diverse functions in cells, is one of the most frequently dysregulated pathways in cancer. Several negative regulators have been reported to intricately constrain the overactivation of PI3K pathway. Phosphatidylinoinosidine-3-kinase interacting protein 1 (PIK3IP1), as a unique transmembrane protein, is a newly discovered negative regulator of PI3K pathway. PIK3IP1 negatively regulates PI3K activity by directly binding to the p110 catalytic subunit of PI3K. It has been reported that PIK3IP1 is frequently low expressed in tumors and autoimmune diseases. In tumor cells and impaired cardiomyocyte, PIK3IP1 inhibits cell proliferation and survival. Consistently, the expression of PIK3IP1 is related with the condition of cancer. In addition, PIK3IP1 inhibits the inflammatory response and immune function via maintaining the quiescent state of immune cells. Thus, low expression of PIK3IP1 represents the severe condition of autoimmune diseases. PIK3IP1 is regulated by transcription factors, epigenetic factors or micro-RNAs to facilitate its normal function in different cellular contexts. This review integrates the total findings on PIK3IP1 in different disease, and summaries the structure, biological functions and regulatory mechanisms of PIK3IP1.

Screening crucial lncRNAs and genes in osteoarthritis by integrated analysis

BACKGROUND

Osteoarthritis (OA) is one of the most frequent chronic diseases with high morbidity worldwide, marked by degradation of the cartilage and bone, joint instability, stiffness, joint space stenosis and subchondral sclerosis. Due to the elusive mechanism of osteoarthritis (OA), we aimed to identify potential markers for OA and explore the molecular mechanisms underlying OA.

METHODS

Expression profiles data of OA were collected from the Gene Expression Omnibus database to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in OA. Functional annotation and protein-protein interaction (PPI) networks were performed. Then, nearby DEmRNAs of DElncRNAs was obtained. Moreover, GO and KEGG pathway enrichment analysis of nearby DEmRNAs of DElncRNAs was performed. Finally, expression validation of selected mRNAs and lncRNAs was performed by quantitative reverse transcriptase-polymerase chain reaction.

RESULTS

In total, 2080 DEmRNAs and 664 DElncRNAs were determined in OA. PI3K-Akt signaling pathway, Endocytosis and Rap1 signaling pathway were significantly enriched KEGG pathways in OA. YWHAB, HSPA8, NEDD4L and SH3KBP1 were four hub proteins in PPI network. The AC093484.4/TRPV2 interact pair may be involved in the occurrence and development of OA.

CONCLUSION

Our study identified several DEmRNAs and DElncRNAs associated with OA. The molecular characters could provide more information for further study on OA.

Differential expression analysis of genes and long non-coding RNAs associated with KRAS mutation in colorectal cancer cells

KRAS mutation is responsible for 40–50% of colorectal cancers (CRCs). RNA-seq data and bioinformatics methods were used to analyze the transcriptional profiles of KRAS mutant (mtKRAS) in comparison with the wild-type (wtKRAS) cell lines, followed by in-silico and quantitative real-time PCR (qPCR) validations. Gene set enrichment analysis showed overrepresentation of KRAS signaling as an oncogenic signature in mtKRAS. Gene ontology and pathway analyses on 600 differentially-expressed genes (DEGs) indicated their major involvement in the cancer-associated signal transduction pathways. Significant hub genes were identified through analyzing PPI network, with the highest node degree for PTPRC. The evaluation of the interaction between co-expressed DEGs and lncRNAs revealed 12 differentially-expressed lncRNAs which potentially regulate the genes majorly enriched in Rap1 and RAS signaling pathways. The results of the qPCR showed the overexpression of PPARG and PTGS2, and downregulation of PTPRC in mtKRAS cells compared to the wtKRAS one, which confirming the outputs of RNA-seq analysis. Further, significant upregualtion of miR-23b was observed in wtKRAS cells. The comparison between the expression level of hub genes and TFs with expression data of CRC tissue samples deposited in TCGA databank confirmed them as distinct biomarkers for the discrimination of normal and tumor patient samples. Survival analysis revealed the significant prognostic value for some of the hub genes, TFs, and lncRNAs. The results of the present study can extend the vision on the molecular mechanisms involved in KRAS-driven CRC pathogenesis.

Epigenetic Regulation of Immune and Inflammatory Responses in Rheumatoid Arthritis

Purpose

Rheumatoid arthritis (RA) is a disease associated with multiple factors. Epigenetics can affect gene expression without altering the DNA sequence. In this study, we aimed to comprehensively analyze epigenetic regulation in RA.

Methods

Using the Gene Expression Omnibus database, we identified a methylation chip, RNA-sequencing, and miRNA microarray for RA. First, we searched for DNA methylation, genes, and miRNAs associated with RA using differential analysis. Second, we determined the regulatory networks for RA-specific methylation, miRNA, and m6A using cross-analysis. Based on these three regulatory networks, we built a comprehensive epigenetic regulatory network and identified hub genes.

Results

Using a differential analysis, we identified 16,852 differentially methylated sites, 4877 differentially expressed genes, and 32 differentially expressed miRNAs. The methylation-expression regulatory network was mainly associated with the PI3K-Akt and T-cell receptor signaling pathways. The miRNA expression regulatory network was mainly related to the MAPK and chemokine signaling pathways. M6A regulatory network was mainly associated with the MAPK signaling pathway. Additionally, five hub genes were identified in the epigenetic regulatory network: CHD3, SETD1B, FBXL19, SMARCA4, and SETD1A. Functional analysis revealed that these five genes were associated with immune cells and inflammatory responses.

Conclusion

We constructed a comprehensive epigenetic network associated with RA and identified core regulatory genes. This study provides a new direction for future research on the epigenetic mechanisms of RA.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Host Blood Gene Signatures Can Detect the Progression to Severe and Cerebral Malaria

Malaria is a major international public health problem that affects millions of patients worldwide especially in sub-Saharan Africa. Although many tests have been developed to diagnose malaria infections, we still lack reliable diagnostic biomarkers for the identification of disease severity, especially in endemic areas where the diagnosis of cerebral malaria is very difficult and requires the exclusion of all other possible causes. Previous host and pathogen transcriptomic studies have not yielded homogenous results that can be harnessed into a reliable diagnostic tool. Here we utilized a multi-cohort analysis approach using machine-learning algorithms to identify blood gene signatures that can distinguish severe and cerebral malaria from moderate and non-cerebral cases. Using a Regularized Random Forest model, we identified 28-gene and 32-gene signatures that can reliably distinguish severe and cerebral malaria, respectively. We tested the specificity of both signatures against other common infectious diseases to ensure the signatures reliability and suitability as diagnostic markers. The severe and cerebral malaria gene-signatures were further integrated through k-top scoring pairs classifiers into ten and nine gene pairs that could distinguish severe and cerebral malaria, respectively. These signatures have various implications that can be utilized as blood diagnostic tools for malaria severity in endemic countries.

Haemophilus parasuis (Glaesserella parasuis) as a Potential Driver of Molecular Mimicry and Inflammation in Rheumatoid Arthritis

Background:Haemophilus parasuis (Hps; now Glaesserella parasuis) is an infectious agent that causes severe arthritis in swines and shares sequence similarity with residues 261–273 of collagen type 2 (Coll_261−273), a possible autoantigen in rheumatoid arthritis (RA).

Objectives/methods: We tested the presence of Hps sequencing 16S ribosomal RNA in crevicular fluid, synovial fluids, and tissues in patients with arthritis (RA and other peripheral arthritides) and in healthy controls. Moreover, we examined the cross-recognition of Hps by Coll_261−273-specific T cells in HLA-DRB1^*04^pos RA patients, by T-cell receptor (TCR) beta chain spectratyping and T-cell phenotyping.

Results:Hps DNA was present in 57.4% of the tooth crevicular fluids of RA patients and in 31.6% of controls. Anti-Hps IgM and IgG titers were detectable and correlated with disease duration and the age of the patients. Peripheral blood mononuclear cells (PBMCs) were stimulated with Hps virulence-associated trimeric autotransporter peptide (VtaA10_755−766), homologous to human Coll_261−273 or co-cultured with live Hps. In both conditions, the expanded TCR repertoire overlapped with Coll_261−273 and led to the production of IL-17.

Discussion: We show that the DNA of an infectious agent (Hps), not previously described as pathogen in humans, is present in most patients with RA and that an Hps peptide is able to activate T cells specific for Coll_261−273, likely inducing or maintaining a molecular mimicry mechanism.

Conclusion: The cross-reactivity between VtaA10_755−766 of a non-human infectious agent and human Coll_261−273 suggests an involvement in the pathogenesis of RA. This mechanism appears emphasized in predisposed individuals, such as patients with shared epitope.

Mining the capacity of human-associated microorganisms to trigger rheumatoid arthritis-A systematic immunoinformatics analysis of T cell epitopes

Autoimmune diseases, often triggered by infection, affect ~5% of the worldwide population. Rheumatoid Arthritis (RA)–a painful condition characterized by the chronic inflammation of joints—comprises up to 20% of known autoimmune pathologies, with the tendency of increasing prevalence. Molecular mimicry is recognized as the leading mechanism underlying infection-mediated autoimmunity, which assumes sequence similarity between microbial and self-peptides driving the activation of autoreactive lymphocytes. T lymphocytes are leading immune cells in the RA-development. Therefore, deeper understanding of the capacity of microorganisms (both pathogens and commensals) to trigger autoreactive T cells is needed, calling for more systematic approaches. In the present study, we address this problem through a comprehensive immunoinformatics analysis of experimentally determined RA-related T cell epitopes against the proteomes of Bacteria, Fungi, and Viruses, to identify the scope of organisms providing homologous antigenic peptide determinants. By this, initial homology screening was complemented with de novo T cell epitope prediction and another round of homology search, to enable: i) the confirmation of homologous microbial peptides as T cell epitopes based on the predicted binding affinity to RA-related HLA polymorphisms; ii) sequence similarity inference for top de novo T cell epitope predictions to the RA-related autoantigens to reveal the robustness of RA-triggering capacity for identified (micro/myco)organisms. Our study reveals a much larger repertoire of candidate RA-triggering organisms, than previously recognized, providing insights into the underestimated role of Fungi in autoimmunity and the possibility of a more direct involvement of bacterial commensals in RA-pathology. Finally, our study pinpoints Endoplasmic reticulum chaperone BiP as the most potent (most likely mimicked) RA-related autoantigen, opening an avenue for identifying the most potent autoantigens in a variety of different autoimmune pathologies, with possible implications in the design of next-generation therapeutics aiming to induce self-tolerance by affecting highly reactive autoantigens.

Integrative Analysis of Human Macrophage Inflammatory Response Related to Mycobacterium tuberculosis Virulence

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, kills 1.5 to 1.7 million people every year. Macrophages are Mtb's main host cells and their inflammatory response is an essential component of the host defense against Mtb. However, Mtb is able to circumvent the macrophages' defenses by triggering an inappropriate inflammatory response. The ability of Mtb to hinder phagolysosome maturation and acidification, and to escape the phagosome into the cytosol, is closely linked to its virulence. The modulation of the host inflammatory response relies on Mtb virulence factors, but remains poorly studied. Understanding macrophage interactions with Mtb is crucial to develop strategies to control tuberculosis. The present study aims to determine the inflammatory response transcriptome and miRNome of human macrophages infected with the virulent H37Rv Mtb strain, to identify macrophage genetic networks specifically modulated by Mtb virulence. Using human macrophages infected with two different live strains of mycobacteria (live or heat-inactivated Mtb H37Rv and M. marinum), we quantified and analyzed 184 inflammatory mRNAs and 765 micro(mi)RNAs. Transcripts and miRNAs differently modulated by H37Rv in comparison with the two other conditions were analyzed using in silico approaches. We identified 30 host inflammatory response genes and 37 miRNAs specific for H37Rv virulence, and highlight evidence suggesting that Mtb intracellular-linked virulence depends on the inhibition of IL-1β-dependent pro-inflammatory response, the repression of apoptosis and the delay of the recruitment and activation of adaptive immune cells. Our findings provide new potential targets for the development of macrophage-based therapeutic strategies against TB.

Comprehensive Integration of Genome-Wide Association and Gene Expression Studies Reveals Novel Gene Signatures and Potential Therapeutic Targets for Helicobacter pylori-Induced Gastric Disease

Helicobacter pylori is a gram-negative bacterium that colonizes the human gastric mucosa and can lead to gastric inflammation, ulcers, and stomach cancer. Due to the increase in H. pylori antimicrobial resistance new methods to identify the molecular mechanisms of H. pylori-induced pathology are urgently needed. Here we utilized a computational biology approach, harnessing genome-wide association and gene expression studies to identify genes and pathways determining disease development. We mined gene expression data related to H. pylori-infection and its complications from publicly available databases to identify four human datasets as discovery datasets and used two different multi-cohort analysis pipelines to define a H. pylori-induced gene signature. An initial Helicobacter-signature was curated using the MetaIntegrator pipeline and validated in cell line model datasets. With this approach we identified cell line models that best match gene regulation in human pathology. A second analysis pipeline through NetworkAnalyst was used to refine our initial signature. This approach defined a 55-gene signature that is stably deregulated in disease conditions. The 55-gene signature was validated in datasets from human gastric adenocarcinomas and could separate tumor from normal tissue. As only a small number of H. pylori patients develop cancer, this gene-signature must interact with other host and environmental factors to initiate tumorigenesis. We tested for possible interactions between our curated gene signature and host genomic background mutations and polymorphisms by integrating genome-wide association studies (GWAS) and known oncogenes. We analyzed public databases to identify genes harboring single nucleotide polymorphisms (SNPs) associated with gastric pathologies and driver genes in gastric cancers. Using this approach, we identified 37 genes from GWA studies and 61 oncogenes, which were used with our 55-gene signature to map gene-gene interaction networks. In conclusion, our analysis defines a unique gene signature driven by H. pylori-infection at early phases and that remains relevant through different stages of pathology up to gastric cancer, a stage where H. pylori itself is rarely detectable. Furthermore, this signature elucidates many factors of host gene and pathway regulation in infection and can be used as a target for drug repurposing and testing of infection models suitability to investigate human infection.

Distinct host-immune response toward species related intracellular mycobacterial killing: A transcriptomic study

The comparison of the host immune response when challenged with pathogenic and nonpathogenic species of mycobacteria can provide answers to the unresolved question of how pathogens subvert or inhibit an effective response. We infected human monocyte derived macrophages (hMDMs) with different species of mycobacteria, in increasing order of pathogenicity, i.e. M. smegmatis, M. bovis BCG, and M. tuberculosis R179 that had been cultured in the absence of detergents. RNA was isolated post-infection and transcriptomic analysis using amplicons (Ampliseq) revealed 274 differentially expressed genes (DEGs) across three species, out of which we selected 19 DEGs for further validation. We used qRT-PCR to confirm the differential expression of 19 DEGs. We studied biological network through Ingenuity Pathway Analysis® (IPA) which revealed up-regulated pathways of the interferon and interleukin family related to the killing of M. smegmatis. Apart from interferon and interleukin family, we found one up-regulated (EIF2AK2) and two down-regulated (MT1A and TRIB3) genes as unique potential targets found by Ampliseq and qRT-PCR which may be involved in the intracellular mycobacterial killing. The roles of these genes have not previously been described in tuberculosis. Multiplex ELISA of culture supernatants showed increased host immune response toward M. smegmatis as compared to M. bovis BCG and M.tb R179. These results enhance our understanding of host immune response against M.tb infection.

Tuberculosis comorbidity with rheumatoid arthritis: Gene signatures, associated biomarkers, and screening

Rheumatoid arthritis (RA) is known to be related to an elevated risk of infections because of its pathobiology and the use of immunosuppressive therapies. Reactivation of latent tuberculosis (TB) infection is a serious issue in patients with RA, especially after receiving anti-TNFs therapy. TNF blocking reinforces the TB granuloma formation and maintenance and the growth of Mycobacterium tuberculosis (Mtb). After intercurrent of TB infection, the standard recommendation is that the treatment with TNF inhibitors to be withheld despite its impressive effect on suppression of inflammation until the infection has resolved. Knowing pathways and mechanisms that are common between two diseases might help to find the mechanistic basis of this comorbidity, as well as provide us a new approach to apply them as therapeutic targets or diagnostic biomarkers. Also, screening for latent TB before initiation of an anti-TNF therapy can minimize complications. This review summarizes the shared gene signature between TB and RA and discusses the biomarkers for early detection of this infection, and screening procedures as well.

Integrated Microarray and RNAseq Transcriptomic Analysis of Retinal Pigment Epithelium/Choroid in Age-Related Macular Degeneration

We report for the first time an integrated transcriptomic analysis of RPE/choroid dysfunction in AMD (mixed stages) based on combining data from publicly available microarray (GSE29801) and RNAseq (GSE135092) datasets aimed at increasing the ability and power of detection of differentially expressed genes and AMD-associated pathways. The analysis approach employed an integrating quantitative method designed to eliminate bias among different transcriptomic studies. The analysis highlighted 764 meta-genes (366 downregulated and 398 upregulated) in macular AMD RPE/choroid and 445 meta-genes (244 downregulated and 201 upregulated) in non-macular AMD RPE/choroid. Of these, 731 genes were newly detected as differentially expressed (DE) genes in macular AMD RPE/choroid and 434 genes in non-macular AMD RPE/choroid compared with controls. Over-representation analysis of KEGG pathways associated with these DE genes mapped revealed two most significantly associated biological processes in macular RPE/choroid in AMD, namely the neuroactive ligand-receptor interaction pathway (represented by 30 DE genes) and the extracellular matrix-receptor interaction signaling pathway (represented by 12 DE genes). Furthermore, protein-protein interaction (PPI) network identified two central hub genes involved in the control of cell proliferation/differentiation processes, HDAC1 and CDK1. Overall, the analysis provided novel insights for broadening the exploration of AMD pathogenesis by extending the number of molecular determinants and functional pathways that underpin AMD-associated RPE/choroid dysfunction.

A survey of gene expression meta-analysis: methods and applications

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.