the-replica-set-method-high-throughput-approach-to-quantitatively

The Replica Set Method: A High-throughput Approach to Quantitatively Measure <em>Caenorhabditis elegans</em> Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus ...

University of Rochester Medical Center

Non-synonymous single nucleotide polymorphisms (nsSNPs) are the most common DNA sequence variation associated with disease in humans. Thus determining the clinical significance of each nsSNP is of great importance. Potential detrimental nsSNPs may be identified by genetic association studies or by functional analysis in the laboratory, both of which are expensive and time consuming. Existing computational methods lack accuracy and features to facilitate nsSNP classification for clinical use. We developed the GESPA (GEnomic Single nucleotide Polymorphism Analyzer) program to predict the pathogenicity and disease phenotype of nsSNPs.

GESPA: classifying nsSNPs to predict disease association.

Comparative analysis of genome-wide expression profiles are increasingly being used to study virus-specific host interactions. In order to gain mechanistic insights, gene expression profiles can be combined with information on DNA-binding sites of transcription factors to detect transcription factor activity (by analysis of target gene sets) during viral infections. Here, we apply this approach to study mechanisms of immune antagonism elicited by Influenza A virus (New Caledonia/20/1999) by comparing the transcriptional response with the non-pathogenic Newcastle disease virus (NDV), which lacks human immune antagonism.

Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism.

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties. 

Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice.

Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT--a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a "template" mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter-sample differences is increased by "competition" wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age-related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified. Alternative algorithms detected far fewer significantly altered clusters. Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets.

Competitive SWIFT cluster templates enhance detection of aging changes.

Systems virology integrates host-directed approaches with molecular profiling to understand viral pathogenesis. Self-contained statistical approaches that combine expression profiles of genes with the available databases defining the genes involved in the pathways (gene-sets) have allowed characterization of predictive gene-signatures associated with outcome of the influenza virus (IV) infection. However, such enrichment techniques do not take into account interactions among pathways that are responsible for the IV infection pathogenesis. We investigate dendritic cell response to seasonal H1N1 influenza A/New Caledonia/20/1999 (NC) infection and infer the Boolean logic rules underlying the interaction network of ligand induced signaling pathways and transcription factors. The model reveals several novel regulatory modes and provides insights into mechanism of cross talk between NFκB and IRF mediated signaling. Additionally, the logic rule underlying the regulation of IL2 pathway that was predicted by the Boolean model was experimentally validated. Thus, the model developed in this paper integrates pathway analysis tools with the dynamic modeling approaches to reveal the regulation between signaling pathways and transcription factors using genome-wide transcriptional profiles measured upon influenza infection. 

Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection.

Recently, an avian influenza virus, H5NX subclade 2.3.4.4, emerged and spread to North America. This subclade has frequently reassorted, leading to multiple novel viruses capable of human infection. Four cases of human infections, three leading to death, have already occurred. Existing vaccine strains do not protect against these new viruses, raising a need to identify new vaccine candidate strains. We have developed a novel sequence-based mapping (SBM) tool capable of visualizing complex protein sequence data sets using a single intuitive map. We applied SBM on the complete set of avian H5 viruses in order to better understand hemagglutinin protein variance amongst H5 viruses and identify any patterns associated with this variation. The analysis successfully identified the original reassortments that lead to the emergence of this new subclade of H5 viruses, as well as their known unusual ability to re-assort among neuraminidase subtypes. In addition, our analysis revealed distinct clusters of 2.3.4.4 variants that would not be covered by existing strains in the H5 vaccine stockpile. The results suggest that our method may be useful for pandemic candidate vaccine virus selection. 

Novel Sequence-Based Mapping of Recently Emerging H5NX Influenza Viruses Reveals Pandemic Vaccine Candidates.

The aim of this study was to investigate whether serum samples from the Department of Defense Serum Repository (DoDSR) are of sufficient quality to detect microRNAs (miRNAs), cytokines, immunoglobulin E (IgE), and polycyclic aromatic hydrocarbons (PAHs).

Detection of Serum microRNAs From Department of Defense Serum Repository: Correlation With Cotinine, Cytokine, and Polycyclic Aromatic Hydrocarbon Levels.

To determine if service members deployed to locations with open air burn pits have different serum microRNA (miRNA) profiles after deployment compared with length-of-service matched, non-deployed individuals. We also tested for correlations between miRNA and serum levels of Polychlorinated Dibenzo-p-Dioxins/Dibenzofurans (PCDD/PCDFs).

MicroRNAs as Novel Biomarkers of Deployment Status and Exposure to Polychlorinated Dibenzo-p-Dioxins/Dibenzofurans.

Pathogen specific immune response is a complex interplay between several innate and adaptive immune cell-types. Innate immune cells play a critical role in pathogen recognition and initiating the antigen specific adaptive immune response. Despite specific functional roles of the innate immune cells, they share several anti-viral pathways. The question then becomes, what is the overlap in the transcriptional changes induced upon viral infections across different cell-types? Here we investigate the extent to which gene signatures are conserved across innate immune cell-types by performing a comparative analysis of transcriptomic data. Particularly, we integrate transcriptomic datasets measuring response of two innate immune cells (epithelial and dendritic cells) to influenza virus. The study reveals cell-type specific regulatory genes and a conserved network between the two cell-types. Additionally, novel functionally associated gene clusters are identified which are robustly defined across multiple independent studies. These gene clusters can be used in future investigation, and to facilitate their use we release PathCellNet (version 0), a cloud based tool to explore cell-type specific connectivity of user-defined genes. In the future, expansion of PathCellNet will allow exploration of cell-type specific responses across a variety of pathogens and cell-types.

PathCellNet: Cell-type specific pathogen-response network explorer.

Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing.

FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.

Leukocyte extravasation is a crucial feature of the normal immune response to disease and infection and is implicated in various pathologies during chronic inflammatory disease. P-Selectin glycoprotein ligand-1 (PSGL-1) is critical for leukocyte extravasation; however, despite extensive study, it remains unclear how its expression is regulated, which in turn, impedes a more precise understanding of how its expression level affects transmigration. To investigate the regulation of PSGL-1, 60 subjects, with or without HIV infection, were recruited and PSGL-1 expression in monocytes was measured. PSGL-1 was found to be up-regulated on leukocytes from HIV-infected individuals, and the physiologically relevant mediators soluble CD40 ligand (sCD40L) and glutamate were able to induce PSGL-1 transcription in human monocytes ex vivo. HIV-1 induced PSGL-1 induction, and its dependence on CD40L was validated further by use of the mouse-tropic HIV (EcoHIV) mouse model of HIV infection in C57BL/6 and CD40L knockout (KO) mice. To investigate crosstalk between the signaling cascades induced by CD40L and glutamate that lead to PSGL-1 induction, a network-based, discrete dynamic model was developed. The model reveals the MAPK pathway and oxidative stress as critical mediators of crosstalk between CD40L and glutamate-induced pathways. Importantly, the model predicted induction of the c-Myc transcription factor upon cotreatment, which was validated using transcriptomic data and pharmacologic inhibition of c-Myc. This study suggests a novel systems serology approach for translational research and reveals a mechanism for PSGL-1 transcriptional regulation, which might be leveraged to identify novel targets for therapeutic intervention.

Frontline Science: c-Myc regulates P-selectin glycoprotein ligand-1 expression in monocytes during HIV-1 infection.

Nearly all children are infected with respiratory syncytial virus (RSV) within the first 2 years of life, with a minority developing severe disease (1%-3% hospitalized). We hypothesized that an assessment of the adaptive immune system, using CD4+ T-lymphocyte transcriptomics, would identify gene expression correlates of disease severity.

Association of Dynamic Changes in the CD4 T-Cell Transcriptome With Disease Severity During Primary Respiratory Syncytial Virus Infection in Young Infants.

Article	Year
GESPA: classifying nsSNPs to predict disease association. BMC bioinformatics\| PubMed ID: 26206375	2015
Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism. BMC immunology\| PubMed ID: 26272204	2015
Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice. PloS one\| PubMed ID: 26413862	2015
Competitive SWIFT cluster templates enhance detection of aging changes. Cytometry. Part A : the journal of the International Society for Analytical Cytology\| PubMed ID: 26441030	2016
Boolean Modeling of Cellular and Molecular Pathways Involved in Influenza Infection. Computational and mathematical methods in medicine\| PubMed ID: 26981147	2016
Novel Sequence-Based Mapping of Recently Emerging H5NX Influenza Viruses Reveals Pandemic Vaccine Candidates. PloS one\| PubMed ID: 27494186	2016
Detection of Serum microRNAs From Department of Defense Serum Repository: Correlation With Cotinine, Cytokine, and Polycyclic Aromatic Hydrocarbon Levels. Journal of occupational and environmental medicine\| PubMed ID: 27501106	2016
MicroRNAs as Novel Biomarkers of Deployment Status and Exposure to Polychlorinated Dibenzo-p-Dioxins/Dibenzofurans. Journal of occupational and environmental medicine\| PubMed ID: 27501109	2016
PathCellNet: Cell-type specific pathogen-response network explorer. Journal of immunological methods\| PubMed ID: 27659011	2016
FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis. Bioinformatics (Oxford, England)\| PubMed ID: 28334094	2017
Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes. BMC bioinformatics\| PubMed ID: 28587632	2017
Frontline Science: c-Myc regulates P-selectin glycoprotein ligand-1 expression in monocytes during HIV-1 infection. Journal of leukocyte biology\| PubMed ID: 28663244	2017
Association of Dynamic Changes in the CD4 T-Cell Transcriptome With Disease Severity During Primary Respiratory Syncytial Virus Infection in Young Infants. The Journal of infectious diseases\| PubMed ID: 28962005	2017

Juilee Thakar

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