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Innate immune defects in HIV permissive cell lines
© The Author(s) 2016
Received: 11 February 2016
Accepted: 14 June 2016
Published: 27 June 2016
Primary CD4+ T cells and cell lines differ in their permissiveness to HIV infection. Impaired innate immunity may contribute to this different phenotype.
We used transcriptome profiling of 1503 innate immunity genes in primary CD4+ T cells and permissive cell lines. Two clusters of differentially expressed genes were identified: a set of 249 genes that were highly expressed in primary cells and minimally expressed in cell lines and a set of 110 genes with the opposite pattern. Specific to HIV, HEK293T, Jurkat, SupT1 and CEM cell lines displayed unique patterns of downregulation of genes involved in viral sensing and restriction. Activation of primary CD4+ T cells resulted in reversal of the pattern of expression of those sets of innate immunity genes. Functional analysis of prototypical innate immunity pathways of permissive cell lines confirmed impaired responses identified in transcriptome analyses.
Integrity of innate immunity genes and pathways needs to be considered in designing gain/loss functional genomic screens of viral infection.
The success of viral replication depends on the ability of a virus to exploit host cell components and evade host cell defense and is thus affected by the specific cellular environment [1–3]. In the past decades, studies on HIV have often used established T cell lines to investigate the molecular mechanisms of viral replication and virus-host interactions because of ease of infection. In contrast, resting primary CD4+ T cells are difficult to infect and need to be activated through T-Cell Receptor (TCR) stimulation in order to enhance their susceptibility to HIV infection [4, 5]. Despite stimulation, infection efficiency of primary cells rarely reaches the level observed in T cell lines, suggesting that the cellular environment is different. Many transformed cell lines show global or narrow defects in the Interferon (IFN) response, including deficits in signaling (eg., JAK1, [6, 7]) and in expression of transcription factors (eg., ISGF3 and STAT1, [8–12]). A reduced responsiveness to IFN due to defects in the type I IFN pathway is also a common hallmark among malignant cells . There is ample evidence of innate immunity defects in cell lines used in HIV research. For example, HEK293T do not express detectable levels of STING  or TLRs . A link between antiviral defense and cell proliferation has been proposed for HEK293T and HeLa cells. Here, immune defects in the cGas-STING pathway may be explained by the constitutive expression of viral oncoproteins that interfere with the innate immune response while at the same time supporting cell transformation . Although still responsive to IFN-α in terms of general ISG expression, Jurkat cells are unable to induce the expression of specific antiviral genes . SupT1 and other cell lines exhibit a diversity of patterns in response to interferon . Limited response to IFN-α and high permissiveness to HIV infection led to the identification of MX2 in CEM cells . Thus, lack of integrity of the IFN pathway or the failure to induce antiviral factors is thought to underlie the ease of propagation of numerous viruses in cell lines .
We aimed at characterizing the transcriptional landscape of innate immunity genes in cell lines used in HIV research, to contrast them with primary CD4+ T cells, and to validate some of the defects in functional assays. The emerging picture is that of a diversity of patterns of downregulation that support the concept of innate immunity defects underlying the high permissiveness of commonly used cell lines to HIV.
Innate immunity gene expression in primary cells and in HIV-permissive cell lines
To identify innate immunity genes associated with HIV permissiveness at the cellular level, we focused on genes that were differentially expressed in resting CD4+ T cells compared to cell lines. Two clusters corresponding to such criteria were identified. First, a cluster of 249 innate immunity genes that were highly expressed in primary cells and lowly expressed or absent in cell lines (Fig. 1, dark blue cluster, 204 of them with a fold change higher than 2, Benjamini–Hochberg adjusted p value <0.01, Methods). This set included Toll-like receptors (TLRs), NOD-like receptors (NLRs), sialic acid binding Ig-like lectin (SIGLEC) and C-type lectin (CLEC) family members, interleukins and their receptors (ILs and ILRs), chemokine ligands and receptors (CCLs, CXCLs and CCRs), caspases, complement components, MAP-kinases, transcription factors and regulators (Interferon -IFN- regulatory factors -IRFs, JAK/STATs, FOS and JUN) among others (Additional file 3: Table S2). The second cluster included 110 innate immunity genes (Fig. 1, cyan cluster, 101 genes with a fold change higher than 2, Benjamini–Hochberg adjusted p value <0.01) with low expression in resting CD4+ T cells and high expression in cell lines. This cluster contained genes acting as suppressors of the innate immune response (e.g. TYRO3, BIRC5), inhibitors of transcription factors (NKIRAS2 -inhibitor of NFKB- and PIAS4 -inhibitor of activated STAT4) and transcriptional repressors (HES4, CSDA, RCOR1) (Additional file 3: Table S2). Of note, excluding samples infected with HIV or exposed to heat-inactivated HIV from the analysis led to a highly similar clustering of genes (Spearman correlation of gene distances between the two clusterings of 0.96, p value <2.2e−16, Additional file 4: Figure S2), reproducing the same two clusters.
Effect of CD4+ T cell activation on relevant innate immunity genes
Transcriptional and functional defects in innate immunity pathways in cell lines
We used functional assays to evaluate the consequences of diminished expression of genes involved in those selected pathways applying specific stimuli and recording the corresponding read-outs, i.e. expression of specific effectors or activation of STAT1 (Fig. 3; Additional file 10: Table S3). Consistent with the absence or reduced expression of TLR7 and TLR8 in permissive cell lines (Fig. 3a), stimulation of the TLR pathway with R848 failed to increase IL6 mRNA as measured by RT-qPCR, and in contrast to resting CD4+ T cells. As expected from the transcriptional integrity of the core STAT-dependent signaling of the IFN-gamma pathway, the addition of IFN-gamma to both resting CD4+ T cells and cell lines resulted in the successful phosphorylation of STAT1 (Additional file 11: Figure S8) and increased expression of IRF1 mRNA (Fig. 3b; Additional file 10: Table S3). IFN-gamma stimulation failed to result in detectable expression of IL1B mRNA in cell lines, consistent with low expression levels of key components in this cascade (e.g. IRF4; Additional file 8: Figure S6). In the case of the TNF-alpha signaling pathway, the integrity of the signaling cascade in cell lines at the transcriptional level was coherent with the detection of BIRC3 by RT-qPCR upon addition of TNF-alpha (Fig. 3b; Additional file 10: Table S3). However, only SupT1 cells displayed an increase of IL6.
Expression of genes involved in HIV sensing and restriction
The innate immune response differs according to the cell type or cell state, such as activated vs resting CD4+ T cells, and this may in turn affect the outcome of viral infection [5, 29, 30]. Activated CD4+ T cells are more permissive to HIV infection in part because of reduced innate immune responses. This favors productive infection and virus-induced cell death by apoptosis. In contrast, resting CD4+ T cells are more resistant to HIV-1 infection, thanks to expression of innate immune defenses (SAMHD1-mediated impaired reverse transcription, IFI16-mediated viral nucleic acid sensing and signaling), leading to abortive infection and to cell death induced by pyroptosis (although this is not observed in vitro upon cell-free virus infection). Changes in expression of innate immunity signaling and effector molecules impact the model of cell death induced by HIV-1 infection, whether triggered by apoptosis or pyroptosis . Therefore, the cell lines used to investigate viral infection may only partially reflect physiological innate immune responses.
Overall, our results show that permissive laboratory cell lines have transcriptional and functional defects in components of key innate immunity signaling pathways resulting in reduced activation or absence of effector gene expression upon specific stimulation. Such defects may contribute to the success of viral infection in cell lines compared to primary cells. This study supports the call for caution when investigating the interaction between viral and innate immunity factors using cell lines. Furthermore, it provides criteria for the choice of gain or loss of function screenings of viral infection; i.e., in the absence of expression of a target innate immunity factor, the best screen may be over-expression rather than knockdown.
Primary CD4+ T cells
Peripheral Blood Mononuclear Cells (PBMCs) from two different healthy blood donors were purified by Ficoll gradient separation followed by primary CD4+ T cell isolation using negative selection and magnetic separation (human CD4+ T Cell Isolation kit II; Miltenyi Biotec) as previously described  and directly used for total RNA extraction.
Activated CD4+ T cells
Activated CD4+ T cell data were from Mohammadi et al . Briefly, primary CD4+ T cells isolated from a healthy blood donor were activated using CD3/CD28 co-stimulation in presence of IL-2 (mimicking T-Cell Receptor (TCR) stimulation). For this, anti-CD3 antibodies (10 μg) were plated in 1 ml PBS per well of a 6-well plate and incubated for 1–2 h at 37 °C. Wells were washed once with 3 ml of PBS and filled with 106 cells/ml of primary CD4+ T cells supplemented with 1 μg/ml anti-CD28 antibodies in R-10 culture medium containing 100 U/ml human recombinant IL-2 (R&D Systems). Three days post-stimulation, activated CD4+ T cells (106 cells) were infected or not with 5 μg p24 equivalent of HIVeGFP/VSV-G particles in presence of 2.5 μg/ml polybrene and in 300 μl final volume by spinoculation (1500 g, 25 °c, 2 h). Cells were left to return to a resting state by co-culture on a feeder cell layer for 10 weeks. Mock or infected resting CD4+ T cells were re-activated by TCR stimulation for 8 and 24 h before total RNA extraction.
Three T lymphoblastic cell lines, SupT1, Jurkat and CEM cells were cultured in RPMI 1640 (Invitrogen) supplemented with 10 % FCS and 50 μg/ml gentamicin (R-10 culture medium). Human embryonic kidney (HEK) 293T cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Invitrogen) supplemented with 10 % heat-inactivated fetal calf serum (FCS) and 50 μg/ml gentamicin (D-10 culture medium).
Cell lines were infected or not by HIVeGFP/VSV-G for 24 h before total RNA extraction. Briefly HEK293T cells (200,000 cells) were plated in 12-well plates in 2 ml culture medium and let adhere over night before infection by spinoculation (1500 g, 25 °C, 3 h) with 1 μg p24 equivalent of HIVeGFP/VSV-G particles in presence of 20 μg/ml polybrene. SupT1, Jurkat and CEM T cell lines (106 cells) were infected with 3 μg p24 equivalent of HIVeGFP/VSV-G particles in presence of 5 μg/ml polybrene and in 400 μl final volume by spinoculation (1500 g, 25 °c, 2 h). As controls, mock infection as well as infection using HIVeGFP/VSV-G heat-inactivated at 56 °C for 60 min were performed in parallel.
HIV-based vector production
HEK293T cells were co-transfected with 15 μg pNL4-3ΔEnv-GFP (NIH AIDS Research and Reference Reagent program, Cat. #11100; ) and 5 μg pMD.G , using the calcium phosphate method (Invitrogen) to generate HIVeGFP/VSV-G particles as described previously . HIV titer was measured by p24 ELISA (Abbott).
RNA seq and bioinformatic analyses
Cell lines infected with HIV-based vector, or mock infected, were collected for RNA extraction (Illustra RNAspin mini isolation kit; GE Healthcare) and subsequent mRNA-Seq transcriptome analysis as described previously . mRNA-Seq library preparation was done with TruSeq RNA sample prep kit (Illumina) starting with capture of polyA-containing transcripts, followed by cluster generation (TruSeq cluster generation kit, Illumina) and high-throughput sequencing on Illumina HiSeq2000 at the Genomics Technology Facility, University of Lausanne. Single read 100 base pairs were performed in all libraries except for primary CD4+ T cells (samples CD4J3 and CD4J4) that were paired-end 100 base pairs. Sequencing data were bioinformatically analyzed as if they were single-end. The 100 bp single-end reads were trimmed and filtered before alignment as described in . RNA-Seq data for activated cells (TCR-stimulated for 8 and 24 h) were from Mohammadi et al. (PLoS Pathogens 2014). Filtered reads were aligned to the human reference genome with RUM aligner (version v2.0.4; ) using the Ensembl gene GRCh37 release 70 annotation file concatenated to the HIV vector sequence used for infection. The number of reads per gene was quantified with HTSeq-count v.0.6.1  with parameters mode = union and type = exon. We obtained an average library size of 77,999,650 uniquely mapped reads. When indicated in downstream analysis, log-transformation of gene expression values was performed as the log10 of the number of library size-normalized reads per kilobase of exonic sequence. A pseudo-count of 1 was added prior to the log10 transformation to avoid NA’s: log10(RPKM*77999650/1,000,000+1); RPKM: Reads per Kilobase per Million mapped Reads. Variance stabilization transformation was performed with R package DESeq  with parameters method “blind” for the computation of the empirical dispersion and fitType = “local” for fitting a dispersion-mean relation. The per-gene raw read-counts matrix and the RPKM matrix are provided as Additional files 12 and 13: Tables S4 and S5, respectively (see also Additional file 14: Table S6). Differential expression analysis between primary cells and cell lines used DESeq2 package . An R script with all statistical analyses performed necessary to reproduce results and figures is provided as Additional file 15: File S1. Versions of R-packages used are detailed in Additional file 16: File S2.
Perimeter of innate immunity genes
A representative list of 1503 human innate immunity genes compiled as described in  was used (Additional file 1: Table S1). The list represents the union of four resources: (1) genes annotated with the term “innate immunity response” (GO:0045087) in the Gene Ontology project (http://www.geneontology.org/) ; (2) innate immunity genes manually annotated in the InnateDB database (http://www.innatedb.ca) ; (3) IFN-stimulated genes from the ISG database  identified through expression analyses, and (4) a list of IFN-stimulated genes used for extensive functional analyses in the context of viral infection, including HIV .1473 of the 1503 innate immunity genes were found expressed in at least one of the resting CD4+ T cell samples or laboratory cell lines analyzed.
Functional analysis of innate immunity pathway
SupT1, Jurkat and CEM T cells lines (106 cells) were seeded in 12-well plates in 1 ml R-10 culture medium in presence of two concentrations of compound. Functional analyses used 1 and 10 μg/ml R848 (ligand of TLR7/8), 0.1 and 1 μM CpG oligodeoxynucleotide (ligand of TLR9), 10 and 100 U/ml IFN-γ, 5 and 25 ng/ml TNF, and 1 and 10 μg/ml MDP (ligand of NOD2). Mock treatment was used as negative control. After 1, 4 and 24 h of incubation, cells were collected for downstream analyses: total RNA extraction (Illustra RNAspin mini isolation kit; GE Healthcare) and gene expression analysis by RT-qPCR, or for cell lysis and immunoblot analysis. Primary resting CD4+ T cells (106 cells) isolated from two healthy blood donors were tested similarly in R-10 culture medium only (negative control), in presence of 10 μg/ml R848, 100 and 1000 U/ml IFN-γ, or TCR stimulation (anti-CD3/anti-CD28/IL2) for 24 h (Additional files 17 and 18: File S3 and S4).
Gene expression analysis
RNA (200 ng) was reverse transcribed using High-Capacity cDNA Reverse Transcription (Life Technologies) according to manufacturer’s instructions in a total volume of 20 μl. Eight genes representative of the selective pathways were assessed in duplicate by qPCR using 2 μl cDNA, and commercially available Gene Expression Assays with FAM-MGB probes (Applied Biosystems) following manufacturer’s recommendations: IL1B (Hs01555410_m1), IRF1 (Hs00971960_m1), IFNB1 (Hs01077958_s1), IL12B (Hs01011518_m1), IL6 (Hs00985639_m1), CASP1 (Hs00236158_m1), IL18 (Hs01038788_m1) and BIRC3 (Hs00985031_g1). PIGS mRNA with a VIC-MGB probe (Hs00264209_m1) was used as endogenous control. qPCR was carried out in a StepOnePlus (Applied Biosystems) using the following cycling conditions: 2′ at 50 °C, 10′ at 95 °C, 40 cycles of 15″ at 95 °C and 1’ at 60 °C. Calculations were ΔΔCT = (CT gene − CT PIGS)compound − (CT gene – CT PIGS)mock. Log2 fold change of RT-qPCR data of compound over mock treated samples corresponds to the −ΔΔCT.
Cells (106) were lysed in RIPA buffer (50 mM Tris-HCl [pH 8.0], 150 mM NaCl, 2 mM EDTA, 1 % NP-40, 0.1 % SDS, 0.5 % sodium deoxycholate) supplemented with protease inhibitors (Complete Mini; Roche) and phosphatase inhibitors (PhosStop; Roche) for 45′ at 4 °C. Cell lysates were centrifuged and clean supernatants were further used for protein quantification (BCA Protein Assay kit; Pierce) following manufacturer’s instructions. Whole cell lysates (10 μg total proteins) were separated by SDS-polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane. Total phospho-Stat1 (Mouse anti-P-Y701 Stat1, 1:1000, #612132, BD Biosciences) and Stat1 (Rabbit anti-Stat1 antibody, 1:1000, #9172, Primary Cell Signaling Technology) were detected using standard procedures with Tris-Buffered Saline (TBS)-0.2 % Tween-5 % BSA and PBS-0.2 % Tween-5 % milk as blocking buffers for Phospho-Stat1 and Stat1 respectively. Secondary antibodies were rabbit anti-mouse-HRP (1:5000, #P0260, DAKO) and swine anti-rabbit-HRP (1:1000, #P02117, DAKO) respectively. Membrane stripping was performed according to Blot Restore Membrane Rejuvination kit (Millipore) followed by tubulin detection (Mouse anti-tubulin, 1:10,000, #T5168, Sigma). Detection was finalized using ECL chemiluminescence detection (LiteAblot; Euroclone).
Conceived and designed the experiments: AR, AT, AC. Performed the experiments: AR, RM, MM. Analyzed the data: AR, AT, AC. Contributed reagents/materials/analysis tools: TR. Wrote the paper: AR, AT, AC. All authors read and approved the final manuscript.
Authors are thankful to Nitisha Pyndiah for the Western blot experiments shown in Additional file 9: Figure S7. Part of the computations were performed at the Vital-IT (http://www.vital-it.ch) Center for high-performance computing of the SIB Swiss Institute of Bioinformatics.
The authors declare that they have no competing interests.
The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/2007-2013/ under Grant Agreement No. 305762 /Hit Hidden HIV and from the Swiss National Science Foundation Grant No. 149724, 146579 and 149511.
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- Ciuffi A, Bleiber G, Muñoz M, Martinez R, Loeuillet C, Rehr M, et al. Entry and transcription as key determinants of differences in CD4 T-cell permissiveness to human immunodeficiency virus type 1 infection. J Virol. 2004;78:10747–54. doi:10.1128/JVI.78.19.10747-10754.2004.View ArticlePubMedPubMed CentralGoogle Scholar
- Ciuffi A, Bushman FD. Integration. In: Hope TJ, Stevenson M, Richman D, editors. Encyclopedia of AIDS. New York: Springer; 2013. p. 1–10. http://link.springer.com/referenceworkentry/10.1007/978-1-4614-9610-6_66-1.
- Mohammadi P, Desfarges S, Bartha I, Joos B, Zangger N, Muñoz M, et al. 24 hours in the life of HIV-1 in a T cell line. PLoS Pathog. 2013;9:e1003161. doi:10.1371/journal.ppat.1003161.View ArticlePubMedPubMed CentralGoogle Scholar
- Zack JA, Arrigo SJ, Weitsman SR, Go AS, Haislip A, Chen IS. HIV-1 entry into quiescent primary lymphocytes: molecular analysis reveals a labile, latent viral structure. Cell. 1990;61:213–22.View ArticlePubMedGoogle Scholar
- Pan X, Baldauf H-M, Keppler OT, Fackler OT. Restrictions to HIV-1 replication in resting CD4+ T lymphocytes. Cell Res. 2013;23:876–85. doi:10.1038/cr.2013.74.View ArticlePubMedPubMed CentralGoogle Scholar
- Dunn GP, Sheehan KCF, Old LJ, Schreiber RD. IFN unresponsiveness in LNCaP cells due to the lack of JAK1 gene expression. Cancer Res. 2005;65:3447–53. doi:10.1158/0008-5472.CAN-04-4316.PubMedGoogle Scholar
- Ren Y, Zhang Y, Liu RZ, Fenstermacher DA, Wright KL, Teer JK, et al. JAK1 truncating mutations in gynecologic cancer define new role of cancer-associated protein tyrosine kinase aberrations. Sci Rep. 2013;3:3042. doi:10.1038/srep03042.PubMedPubMed CentralGoogle Scholar
- Dron M, Tovey MG. Interferon-resistant Daudi cells are deficient in interferon-alpha-induced ISGF3 alpha activation, but remain sensitive to the interferon-alpha-induced increase in ISGF3 gamma content. J Interferon Res. 1993;13:377–83.View ArticlePubMedGoogle Scholar
- Petricoin E, David M, Fang H, Grimley P, Larner AC, Vande Pol S. Human cancer cell lines express a negative transcriptional regulator of the interferon regulatory factor family of DNA binding proteins. Mol Cell Biol. 1994;14:1477–86.View ArticlePubMedPubMed CentralGoogle Scholar
- Improta T, Pine R, Pfeffer LM. Interferon-gamma potentiates the antiviral activity and the expression of interferon-stimulated genes induced by interferon-alpha in U937 cells. J Interferon Res. 1992;12:87–94.View ArticlePubMedGoogle Scholar
- Eilers A, Seegert D, Schindler C, Baccarini M, Decker T. The response of gamma interferon activation factor is under developmental control in cells of the macrophage lineage. Mol Cell Biol. 1993;13:3245–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Matin SF, Rackley RR, Sadhukhan PC, Kim MS, Novick AC, Bandyopadhyay SK. Impaired alpha-interferon signaling in transitional cell carcinoma: lack of p48 expression in 5637 cells. Cancer Res. 2001;61:2261–6.PubMedGoogle Scholar
- Lin AH, Burrascano C, Pettersson PL, Ibañez CE, Gruber HE, Jolly DJ. Blockade of type I interferon (IFN) production by retroviral replicating vectors and reduced tumor cell responses to IFN likely contribute to tumor selectivity. J Virol. 2014;88:10066–77. doi:10.1128/JVI.02300-13.View ArticlePubMedPubMed CentralGoogle Scholar
- Burdette DL, Monroe KM, Sotelo-Troha K, Iwig JS, Eckert B, Hyodo M, et al. STING is a direct innate immune sensor of cyclic di-GMP. Nature. 2011;478:515–8. doi:10.1038/nature10429.View ArticlePubMedPubMed CentralGoogle Scholar
- Hornung V, Rothenfusser S, Britsch S, Krug A, Jahrsdörfer B, Giese T, et al. Quantitative expression of toll-like receptor 1-10 mRNA in cellular subsets of human peripheral blood mononuclear cells and sensitivity to CpG oligodeoxynucleotides. J Immunol Baltim Md. 1950;2002(168):4531–7.Google Scholar
- Lau L, Gray EE, Brunette RL, Stetson DB. DNA tumor virus oncogenes antagonize the cGAS-STING DNA-sensing pathway. Science. 2015;350:568–71. doi:10.1126/science.aab3291.View ArticlePubMedGoogle Scholar
- Goujon C, Malim MH. Characterization of the alpha interferon-induced postentry block to HIV-1 infection in primary human macrophages and T cells. J Virol. 2010;84:9254–66. doi:10.1128/JVI.00854-10.View ArticlePubMedPubMed CentralGoogle Scholar
- Wathelet MG, Berr PM, Huez GA. Regulation of gene expression by cytokines and virus in human cells lacking the type-I interferon locus. Eur J Biochem FEBS. 1992;206:901–10.View ArticleGoogle Scholar
- Goujon C, Moncorgé O, Bauby H, Doyle T, Ward CC, Schaller T, et al. Human MX2 is an interferon-induced post-entry inhibitor of HIV-1 infection. Nature. 2013;502:559–62. doi:10.1038/nature12542.View ArticlePubMedGoogle Scholar
- Rausell A, McLaren PJ, Telenti A. HIV and innate immunity—a genomics perspective. F1000Prime Rep. 2013;5:29. doi:10.12703/P5-29.View ArticlePubMedPubMed CentralGoogle Scholar
- Mohammadi P, di Iulio J, Muñoz M, Martinez R, Bartha I, Cavassini M, et al. Dynamics of HIV latency and reactivation in a primary CD4+ T cell model. PLoS Pathog. 2014;10:e1004156. doi:10.1371/journal.ppat.1004156.View ArticlePubMedPubMed CentralGoogle Scholar
- Mangeat B, Turelli P, Caron G, Friedli M, Perrin L, Trono D. Broad antiretroviral defence by human APOBEC3G through lethal editing of nascent reverse transcripts. Nature. 2003;424:99–103. doi:10.1038/nature01709.View ArticlePubMedGoogle Scholar
- Ishikawa H, Barber GN. STING is an endoplasmic reticulum adaptor that facilitates innate immune signalling. Nature. 2008;455:674–8. doi:10.1038/nature07317.View ArticlePubMedPubMed CentralGoogle Scholar
- Sheehy AM, Gaddis NC, Choi JD, Malim MH. Isolation of a human gene that inhibits HIV-1 infection and is suppressed by the viral Vif protein. Nature. 2002;418:646–50. doi:10.1038/nature00939.View ArticlePubMedGoogle Scholar
- Neagu MR, Ziegler P, Pertel T, Strambio-De-Castillia C, Grütter C, Martinetti G, et al. Potent inhibition of HIV-1 by TRIM5-cyclophilin fusion proteins engineered from human components. J Clin Invest. 2009;119:3035–47. doi:10.1172/JCI39354.View ArticlePubMedPubMed CentralGoogle Scholar
- Laguette N, Sobhian B, Casartelli N, Ringeard M, Chable-Bessia C, Ségéral E, et al. SAMHD1 is the dendritic- and myeloid-cell-specific HIV-1 restriction factor counteracted by Vpx. Nature. 2011;474:654–7. doi:10.1038/nature10117.View ArticlePubMedPubMed CentralGoogle Scholar
- Schmidt S, Schenkova K, Adam T, Erikson E, Lehmann-Koch J, Sertel S, et al. SAMHD1’s protein expression profile in humans. J Leukoc Biol. 2015;98:5–14. doi:10.1189/jlb.4HI0714-338RR.View ArticlePubMedGoogle Scholar
- Krapp C, Hotter D, Gawanbacht A, McLaren PJ, Kluge SF, Stürzel CM, et al. Guanylate binding protein (GBP) 5 is an interferon-inducible inhibitor of HIV-1 infectivity. Cell Host Microbe. 2016;19:504–14. doi:10.1016/j.chom.2016.02.019.View ArticlePubMedGoogle Scholar
- Doitsh G, Greene WC. Dissecting how CD4 T cells are lost during HIV infection. Cell Host Microbe. 2016;19:280–91. doi:10.1016/j.chom.2016.02.012.View ArticlePubMedGoogle Scholar
- Jakobsen MR, Olagnier D, Hiscott J. Innate immune sensing of HIV-1 infection. Curr Opin HIV AIDS. 2015;10:96–102. doi:10.1097/COH.0000000000000129.View ArticlePubMedGoogle Scholar
- Zhang H, Zhou Y, Alcock C, Kiefer T, Monie D, Siliciano J, et al. Novel single-cell-level phenotypic assay for residual drug susceptibility and reduced replication capacity of drug-resistant human immunodeficiency virus type 1. J Virol. 2004;78:1718–29. doi:10.1128/JVI.78.4.1718-1729.2004.View ArticlePubMedPubMed CentralGoogle Scholar
- Naldini L, Blömer U, Gallay P, Ory D, Mulligan R, Gage FH, et al. In vivo gene delivery and stable transduction of nondividing cells by a lentiviral vector. Science. 1996;272:263–7.View ArticlePubMedGoogle Scholar
- Grant GR, Farkas MH, Pizarro AD, Lahens NF, Schug J, Brunk BP, et al. Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinforma Oxf Engl. 2011;27:2518–28. doi:10.1093/bioinformatics/btr427.Google Scholar
- Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9. doi:10.1093/bioinformatics/btu638.View ArticlePubMedGoogle Scholar
- Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106. doi:10.1186/gb-2010-11-10-r106.View ArticlePubMedPubMed CentralGoogle Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. doi:10.1186/s13059-014-0550-8.View ArticlePubMedPubMed CentralGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25:25–9. doi:10.1038/75556.View ArticlePubMedPubMed CentralGoogle Scholar
- Lynn DJ, Chan C, Naseer M, Yau M, Lo R, Sribnaia A, et al. Curating the innate immunity interactome. BMC Syst Biol. 2010;4:117. doi:10.1186/1752-0509-4-117.View ArticlePubMedPubMed CentralGoogle Scholar
- Der SD, Zhou A, Williams BRG, Silverman RH. Identification of genes differentially regulated by interferon α, β, or γ using oligonucleotide arrays. Proc Natl Acad Sci. 1998;95:15623–8. doi:10.1073/pnas.95.26.15623.View ArticlePubMedPubMed CentralGoogle Scholar
- Schoggins JW, Wilson SJ, Panis M, Murphy MY, Jones CT, Bieniasz P, et al. A diverse range of gene products are effectors of the type I interferon antiviral response. Nature. 2011;472:481–5. doi:10.1038/nature09907.View ArticlePubMedPubMed CentralGoogle Scholar
- Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen EL, Bohler A, et al. WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 2016;44:D488–94. doi:10.1093/nar/gkv1024.View ArticlePubMedGoogle Scholar
- Kutmon M, van Iersel MP, Bohler A, Kelder T, Nunes N, Pico AR, et al. PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol. 2015;11:e1004085. doi:10.1371/journal.pcbi.1004085.View ArticlePubMedPubMed CentralGoogle Scholar