Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler

Abstract Background Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. Methods We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. Results The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662–0.989. Three gene sets were not significantly predictive. Conclusion Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.

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Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1574888x13666180105125347 ◽

2018 ◽

Vol 21 (2) ◽

pp. 74-83

Author(s):

Tzu-Hung Hsiao ◽

Yu-Chiao Chiu ◽

Yu-Heng Chen ◽

Yu-Ching Hsu ◽

Hung-I Harry Chen ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cancer Therapy ◽

Cancer Treatment ◽

Cancer Survival ◽

Expression Profiles ◽

Functional Gene ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Data ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Superior Performance ◽

Expression Data ◽

Gene Set ◽

Gene Sets ◽

Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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Systematic Definition and Evolutionary Analsis of Macrophage Genes: Evidence for Novel Host Defense Genes.

Blood ◽

10.1182/blood.v110.11.3856.3856 ◽

2007 ◽

Vol 110 (11) ◽

pp. 3856-3856

Author(s):

Jennifer J. MacDonald ◽

Angel W. Lee ◽

David J. States

Keyword(s):

Gene Expression ◽

Host Defense ◽

Mononuclear Phagocytes ◽

Evolutionary Divergence ◽

Defense Genes ◽

Gene Set ◽

Rates Of Change ◽

Interleukin Receptors ◽

Gene Sets ◽

Go Terms

Abstract Mononuclear phagocytes have intrinsic functions in development and tissue remodeling as well as extrinsic functions in host defense, antigen presentation and innate immunity. To obtain an unbiased view of genes that are characteristic of the macrophage, we compare gene expression in the 15 macrophage gene expression profiling studies (185 samples) with data from 544 platform matched control studies (10,160 samples) using a nonparametric rank based metric. 769 genes show consistently higher expression in both human and mouse macrophage samples defining an unbiased macrophage gene set. Automated natural language processing defines a second group of macrophage related genes. 545 genes are mentioned frequently in sentences containing the word “macrophage” in a PubMed abstract. We refer to these as the literature gene set. 87 genes occurred in both the unbiased and the literature gene sets. Annotations of the unbiased and literature gene sets are remarkably consistent with a 0.93 correlation in the normalized frequency of Gene Ontology (GO) term use in the two sets relative to GO term use for all genes in the genome. The most highly over represented GO terms for macrophage specific genes include: chemotaxis, chemokines and chemokine receptors, inflammation, cytokine and chemokine signaling, JAK-STAT cascade, peroxidase activity. To better define the genes involved in host defense functions, we considered evolutionary behavior. Genes involved in intrinsic host functions show purifying selection and are constrained in their rate of evolutionary change. In contrast, host defense genes (HDG) may be subject to diversifying selection and rapid rates of evolutionary divergence. To control for different rates of mutation at different loci, analysis was based on the rate of nonsynonymous to synonymous mutations (dN/dS). As a group, macrophage genes exhibit higher rates of change (median dN/dS=0.17) compared to all genes in the genome (median dN/dS=0.14, P<1e-6). Cell surface receptors (FCGR3a&b, IL15Ra, CD33, C-type lectin, CD1e and CD54) and secreted ligands (CGA, IL3, osteopontin, CCL1, CXCL13 and CXCL2) dominate the list of most rapidly evolving macrophage genes for both the unbiased and literature gene sets. Variation in dN/dS is seen, even within a single gene with secreted and extracellular domains exhibiting consistently higher rates of change compared to intracellular domains. The GO terms with the highest dN/dS ratios are interleukin receptors, pattern recognition receptors, IgG binding, MHC class I and II and T cell activation. We found many macrophage genes that show high dN/dS ratios including IL15RA, CD33, CSN3, CGA, SPP1, ubiquitin D, CD300a, CXCL13, CD1E, CXCL2, ICAM1, CD34 and APOE. The accelerated rates of evolution of these genes, their expression in the macrophage and often their extracellular location, suggest that these may be novel HDG. Overall, 1064 of the macrophage genes are found in the MiMI database of molecular interactions. 2,493 interactions are found among 683 genes in the macrophage gene set. Thus macrophage specific genes operate as a highly connected network. In summary, we present an unbiased systems biology approach to define genes characteristic of mononuclear phagocytes and macrophage host defense responses, and we identify numerous and novel candidate host defense genes.

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Contribution of NRAMP1 gene expression and protein level in pulmonary and latent TB infection in Indonesia

Journal of Applied Pharmaceutical Science ◽

10.7324/japs.2021.110803 ◽

2021 ◽

Author(s):

Handayani Irda ◽

Djaharuddin Irawaty ◽

Natzir Rosdiana ◽

Arief Mansyur ◽

Ahmad Ahyar ◽

...

Keyword(s):

Gene Expression ◽

Protein Level ◽

Nramp1 Gene ◽

Latent Tb ◽

Latent Tb Infection

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Heme Oxygenase-1 and Neopterin Plasma/Serum Levels and their role in Diagnosing Active and Latent TB among HIV/TB Co-Infected Patients

10.21203/rs.2.14803/v1 ◽

2019 ◽

Author(s):

Uwimaana Esther ◽

Bernard S Bagaya ◽

Barbara Castelnuovo ◽

David P Kateete ◽

Anguzu Godwin ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Heme Oxygenase ◽

Area Under The Curve ◽

Serum Levels ◽

Diagnostic Tools ◽

Heme Oxygenase 1 ◽

Hiv Patients ◽

Receiver Operating Characteristic Curves ◽

Latent Tb ◽

Latent Tb Infection

Abstract Background: Tuberculosis(TB) diagnosis in the presence of HIV co-infection remains challenging. Heme oxygenase 1(HO-1) and neopterin have been validated as potential biomarkers for TB diagnosis. Infection of macrophages with Mycobacterium tuberculosis (M .tb ) causes the production of HO-1 and neopterin and previous studies have shown these to be markers of immune activation. This study was conducted to determine the levels of HO-1 and neopterin and their utility in the diagnosis of TB among individuals enrolled in the Community Health and Social Network of Tuberculosis(COHSONET) study and the Kampala TB Drug Resistance Survey(KDRS). Methods: A total of 210 participants were enrolled in a study of a diagnostic method aimed at determining the levels of HO-1 and neopterin and determine their diagnostic accuracy as biomarkers in TB diagnosis from March to May 2019. M. tb culture was performed on sputum to confirm active TB(ATB) and QuantiFERON TB gold test to confirm latent TB infection(LTBI). ELISAs were performed to determine the levels of HO-1 and neopterin. Data analysis was done using Kruskal Wallis and Receiver Operating Characteristic curves to determine the diagnostic accuracy. Results: HO-1 levels among ATB/HIV patients, LTBI/HIV patients and TB negative individuals were 10.7ng/ml (IQR: 7.3-12.7ng/ml), 7.5ng/ml (IQR: 5.4-14.1ng/ml), 3.3ng/ml (IQR: 2.0-7.1ng/ml) respectively. Neopterin levels among ATB/HIV patients, LTBI/HIV patients and TB negative individuals were 11.7ng/ml (IQR: 5.219.4ng/ml), 8.8ng/ml (IQR: 2.4-19.8ng/ml), and 5.9ng/ml (IQR: 3.410.2ng/ml) respectively. HO-1 showed a sensitivity of 78.57% and a specificity of 71.43% with area under the curve(AUC) of 0.839 when used to diagnose ATB. HO-1 showed AUC of 0.79, sensitivity of 70% and specificity 70% when used to diagnose LTB. Neopterin showed a sensitivity of 61.43% and a specificity of 74.29% with AUC 0.71 when used to diagnose ATB. Neopterin as a biomarker in LTB diagnosis showed AUC of 0.56 which was not significant. Conclusion: HO-1 and neopterin are valuable diagnostic biomarkers for ATB and LTB which could be further utilized to develop less costly rapid diagnostic tools to overcome current TB diagnostic challenges.

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Alterations in the host transcriptome in vitro and in vivo following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection

10.21203/rs.3.rs-37567/v1 ◽

2020 ◽

Author(s):

Xiaomei Lei ◽

Zhijun Feng ◽

Xiaojun Wang ◽

Xiaodong He

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Microarray Data ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set ◽

Gene Sets ◽

Core Genes

Abstract Background. Exploring alterations in the host transcriptome following SARS-CoV-2 infection is not only highly warranted to help us understand molecular mechanisms of the disease, but also provide new prospective for screening effective antiviral drugs, finding new therapeutic targets, and evaluating the risk of systemic inflammatory response syndrome (SIRS) early.Methods. We downloaded three gene expression matrix files from the Gene Expression Omnibus (GEO) database, and extracted the gene expression data of the SARS-CoV-2 infection and non-infection in human samples and different cell line samples, and then performed gene set enrichment analysis (GSEA), respectively. Thereafter, we integrated the results of GSEA and obtained co-enriched gene sets and co-core genes in three various microarray data. Finally, we also constructed a protein-protein interaction (PPI) network and molecular modules for co-core genes and performed Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis for the genes from modules to clarify their possible biological processes and underlying signaling pathway. Results. A total of 11 co-enriched gene sets were identiﬁed from the three various microarray data. Among them, 10 gene sets were activated, and involved in immune response and inflammatory reaction. 1 gene set was suppressed, and participated in cell cycle. The analysis of molecular modules showed that 2 modules might play a vital role in the pathogenic process of SARS-CoV-2 infection. The KEGG enrichment analysis showed that genes from module one enriched in signaling pathways related to inflammation, but genes from module two enriched in signaling of cell cycle and DNA replication. Particularly, necroptosis signaling, a newly identified type of programmed cell death that differed from apoptosis, was also determined in our findings. Additionally, for patients with SARS-CoV-2 infection, genes from module one showed a relatively high-level expression while genes from module two showed low-level. Conclusions. We identified two molecular modules were used to assess severity and predict the prognosis of the patients with SARS-CoV-2 infection. In addition, these results provide a unique opportunity to explore more molecular pathways as new potential targets on therapy in COVID 19.

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Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning

Bioinformatics ◽

10.1093/bioinformatics/btz446 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5137-5145 ◽

Cited By ~ 3

Author(s):

Onur Dereli ◽

Ceyda Oğuz ◽

Mehmet Gönen

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Multiple Kernel Learning ◽

Kernel Learning ◽

Supplementary Information ◽

Support Vector ◽

Gene Set ◽

Multiple Kernel ◽

Pathway Gene ◽

Gene Sets

Abstract Motivation Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used). Availability and implementation Our implementations of survival SVM and Path2Surv algorithms in R are available at https://github.com/mehmetgonen/path2surv together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.

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Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG

PLoS ONE ◽

10.1371/journal.pone.0043301 ◽

2012 ◽

Vol 7 (8) ◽

pp. e43301 ◽

Cited By ~ 6

Author(s):

Ryan Abo ◽

Gregory D. Jenkins ◽

Liewei Wang ◽

Brooke L. Fridley

Keyword(s):

Gene Expression ◽

Genetic Variation ◽

Gene Set ◽

Novel Gene ◽

Gene Sets

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ID: 135: SINGLE CELL GENE EXPRESSION STUDIES IN LUPUS MONOCYTES REVEAL A UNIQUE ANTII-INFLAMMATORY NON-CLASSICAL MONOCYTE POPULATION ASSOCIATED WITH CLINICAL QUIESCENCE

Journal of Investigative Medicine ◽

10.1136/jim-2016-000120.138 ◽

2016 ◽

Vol 64 (4) ◽

pp. 977.2-977

Author(s):

Z Jin ◽

MA Jensen ◽

JM Dorschner ◽

DM Vsetecka ◽

S Amin ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Capture ◽

Gene Set ◽

Inflammatory Gene ◽

Gene Sets ◽

Cell Gene Expression ◽

Induced Genes ◽

Cell Gene ◽

Classical Monocytes

BackgroundOur previous studies have shown that different cell types from the same blood sample demonstrate diverse gene expression parameters. In follow up work, it seems that this diversity extends to cells of the same type from the same blood sample. In this study, we examine single cell gene expression in SLE patient monocytes and determine correlations with clinical features.MethodsCD14++CD16− classical monocytes (CLs) and CD14dimCD16+ non-classical monocytes (NCLs) from SLE patients were purified by magnetic separation. The Fluidigm single cell capture and pre-amplification system was used for single cell capture and target gene pre-amplification. Fluidigm Biomark system (Rt-PCR system) was used to quantify expression of 87 monocyte-related genes. IFN-induced genes in monocytes were identified by culturing monocytes isolated from whole blood of healthy controls with or without IFN-α. Genes significant up-regulated by IFN were identified as IFN-induced genes in current study. An individual cell IFN score was given based upon the sum of expression of IFN-induced genes.ResultsBoth CLs and NCLs demonstrated a wide range of expression of IFN-induced genes, and NCL monocytes had higher IFN scores than CL monocytes. Using unsupervised hierarchical clustering, we found four gene sets that clustered monocytes functionally. These included an IFN-induced gene set, two inflammatory gene sets, and one immunosuppressive gene set. Interestingly, we could define a large subset of NCL monocytes with upregulation of suppressive transcripts (including TGF-β and PDL1) and IFN-induced transcripts were also upregulated, while the two inflammatory gene sets were down-regulated. These cells were highly over-represented in a patient with inactive disease who was on immunosuppressants at the time of blood draw. The proportion of anti-inflammatory gene set expressing NCLs was inversely correlated with anti-dsDNA titers (rho=−0.77, p=0.0051) and positively correlated with C3 complement (rho=0.68, p=0.030) in the SLE patient group, suggesting that these cells are also associated with serological quiescence.ConclusionUsing single cell gene expression, we have identified a unique population of NCL monocytes in SLE patients with upregulation of a combination of anti-inflammatory and IFN-induced transcripts. These cells correspond with clinical and serological quiescence.

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