Interferon regulatory factors 3 and 7 have distinct roles in the pathogenesis of alphavirus encephalomyelitis

Schultz, Kimberly L. W.; Troisi, Elizabeth M.; Baxter, Victoria K.; Glowinski, Rebecca M; Griffin, Diane E.

doi:10.1099/jgv.0.001174

Cited by 16 publications

(15 citation statements)

References 123 publications

(154 reference statements)

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“…GABPA is essential for human cognition (Reiff et al, 2014). IRF3 is related to brain disease (Schultz et al, 2019). PPAR, which PPARG belongs to, is believed to be the therapeutic target of neurodegenerative diseases (Warden et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Taguchi

Turki

2019

Front. Genet.

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Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clustering, for example, t-distributed stochastic neighbor embedding and uniform manifold approximation and projection, is usually employed to obtain low-dimensional embedding that can help to understand cell–cell relationship. One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. In order to fulfill this requirement, there are many methods that performed unsupervised gene selection. In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development. TD-based unsupervised FE could select not only coincident genes between human and mouse but also biologically reliable genes. Coincidence between two species as well as biological reliability of selected genes is increased compared with that using principal component analysis (PCA)-based FE applied to the same data set in the previous study. Since PCA-based unsupervised FE outperformed the other three popular unsupervised gene selection methods, highly variable genes, bimodal genes, and dpFeature, TD-based unsupervised FE can do so as well. In addition to this, 10 transcription factors (TFs) that might regulate selected genes and might contribute to midbrain development were identified. These 10 TFs, BHLHE40, EGR1, GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to be related to brain functions and diseases. TD-based unsupervised FE is a promising method to integrate two scRNA-seq profiles effectively.

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Section: Resultsmentioning

confidence: 99%

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Taguchi

Turki

2019

Front. Genet.

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“…GABPA is essential for human cognition (16). IRF3 is related to brain disease (18). PPAR, which PPARG belongs to, is believed to be therapeutic target of neurodegenerative diseases (53).…”

Section: Resultsmentioning

confidence: 99%

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Taguchi

Turki

2019

Preprint

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3Although single cell RNA sequencing (scRNA-seq) technology is newly invented and promising 4 one, because of lack of enough information that labels individual cells, it is hard to interpret 5 the obtained gene expression of each cell. Because of this insufficient information available, 6 unsupervised clustering, e.g., tSNE and UMAP, is usually employed to obtain low dimensional 7 embedding that can help to understand cell-cell relationship. One possible drawback of this 8 strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. 9 In order to fulfill this requirements, there are many methods that performed unsupervised gene 10 selection. In this study, a tensor decomposition (TD) based unsupervised feature extraction 11 (FE) was applied to the integration of two scRNA-seq expression profiles that measure human 12 and mouse midbrain development. TD based unsupervised FE could not only select coincident 13 genes between human and mouse, but also biologically reliable genes. Coincidence between two 14 species as well as biological reliability of selected genes is increased compared with principal 15 component analysis (PCA) based FE applied to the same data set in the previous study. Since 16 PCA based unsupervised FE outperformed other three popular unsupervised gene selection 17 methods, highly variable genes, bimodal genes and dpFeature, TD based unsupervised FE can 18 do so as well. In addition to this, ten transcription factors (TFs) that might regulate selected genes 19 and might contribute to midbrain development are identified. These ten TFs, BHLHE40, EGR1, 20 GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to 21 be related to brain functions and diseases. TD based unsupervised FE is promising method to 22 integrate two scRNA-seq profiles effectively. 23 Keywords: tensor decomposition, enrichment analysis, sngle cell RNA-sequencing, midbrain development, inter-species analysis 24 INTRODUCTIONSingle cell RNA sequencing (scRNA-seq) (17) is a newly invented technology that enables us to measure 25 amount of RNA in single cell basis. In spite of its promising potential, it is not easy to interpret the 26 measurements. The primary reason of this difficulty is the lack of sufficient information that characterizes 27 1 Y-h. Taguchi TD based FE to single-cell individual cells. In contrast to the huge number of cells measured, which is often as many as several 28 thousands, the number of labeling is limited, e.g., measurement of conditions as well as the amount of 29 expression of key genes measured by fluorescence-activated cell sorting, whose number is typically as little 30 as tens. This prevents us from selecting genes that characterize the individual cell properties. 31In order to deal with samples without suitable number of labelling, unsupervised method is frequently 32 used, since it does not make use of labeling information directly. K-means clustering as well as hierarchical 33 clustering are the popular methodology tha...

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“…As we have previously found TF mutants to have altered virion morphology secondary to aberrant budding (11), we wanted to investigate in vivo trafficking defects as a possible explanation for this loss of virulence. We employed a second well-established model of infection, IC infection of young C57BL/6 mice (50,51). Again, we found that all three mutant viruses had severely attenuated virulence in WT mice ( Figure 2C).…”

Section: Palmitoylation Of Tf Is a Critical Virulence Determinant Prmentioning

confidence: 97%

TF protein of Sindbis virus antagonizes host type I interferon responses in a palmitoylation-dependent manner

Rogers

Jones-Burrage

Maury

et al. 2019

Preprint

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150 words): Sindbis virus (SINV) produces the small membrane protein TF from the 6K gene via a (-1) programmed ribosomal frameshifting. While several groups have shown that TF-deficient virus exhibits reduced virulence, mechanism(s) by which this occurs remain unknown. Here, we demonstrate a role for TF in antagonizing the host interferon response. Using wild-type and type 1 interferon receptor-deficient mice and primary cells derived from these animals, we show that TF controls the induction of the host interferon responses at early times during infection. Loss of TF production leads to elevated interferon and a concurrent reduction in viral loads with a loss of pathogenicity. Palmitoylation of TF has been shown to be important for particle assembly and morphology. We find that palmitoylation of TF also contributes to the ability of TF to antagonize host interferon responses as dysregulated palmitoylation of TF reduces virulence in a manner similar to loss of TF.

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Interferon regulatory factors 3 and 7 have distinct roles in the pathogenesis of alphavirus encephalomyelitis

Cited by 16 publications

References 123 publications

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

TF protein of Sindbis virus antagonizes host type I interferon responses in a palmitoylation-dependent manner

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