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Differential levels of IFNα subtypes in autoimmunity and viral infection.

ABSTRACT: Type I interferons are essential for host response to viral infections, while dysregulation of their response can result in autoinflammation or autoimmunity. Among IFNα (alpha) responses, 13 subtypes exist that signal through the same receptor, but have been reported to have different effector functions. However, the lack of available tools for discriminating these closely related subtypes, in particular at the protein level, has restricted the study of their differential roles in disease. We developed a digital ELISA with specificity and high sensitivity for the IFNα2 subtype. Application of this assay, in parallel with our previously described pan-IFNα assay, allowed us to study different IFNα protein responses following cellular stimulation and in diverse patient cohorts. We observed different ratios of IFNα protein responses between viral infection and autoimmune patients. This analysis also revealed a small percentage of autoimmune patients with high IFNα2 protein measurements but low pan-IFNα measurements. Correlation with an ISG score and functional activity showed that in this small sub group of patients, IFNα2 protein measurements did not reflect its biological activity. This unusual phenotype was partly explained by the presence of anti-IFNα auto-antibodies in a subset of autoimmune patients. This study reports ultrasensitive assays for the study of IFNα proteins in patient samples and highlights the insights that can be obtained from the use of multiple phenotypic readouts in translational and clinical studies.

SUBMITTER: Bondet V

PROVIDER: S-EPMC7614897 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Differential levels of IFNα subtypes in autoimmunity and viral infection.

Bondet Vincent V Rodero Mathieu P MP Posseme Céline C Bost Pierre P Decalf Jérémie J Haljasmägi Liis L Bekaddour Nassima N Rice Gillian I GI Upasani Vinit V Herbeuval Jean-Philippe JP Reynolds John A JA Briggs Tracy A TA Bruce Ian N IN Mauri Claudia C Isenberg David D Menon Madhvi M Hunt David D Schwikowski Benno B Mariette Xavier X Pol Stanislas S Rozenberg Flore F Cantaert Tineke T Eric Gottenberg J J Kisand Kai K Duffy Darragh D

Cytokine 20210430

Type I interferons are essential for host response to viral infections, while dysregulation of their response can result in autoinflammation or autoimmunity. Among IFNα (alpha) responses, 13 subtypes exist that signal through the same receptor, but have been reported to have different effector functions. However, the lack of available tools for discriminating these closely related subtypes, in particular at the protein level, has restricted the study of their differential roles in disease. We de ...[more]

PMID: 33941444

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Project description:The Type I Interferons (IFN-Is) are innate antiviral cytokines that include 12 different IFNα subtypes and IFNβ that signal through the IFN-I receptor (IFNAR), inducing hundreds of IFN-stimulated genes (ISGs) that comprise the 'interferome'. Quantitative differences in IFNAR binding correlate with antiviral activity, but whether IFN-Is exhibit qualitative differences remains controversial. Moreover, the IFN-I response is protective during acute HIV-1 infection, but likely pathogenic during the chronic stages. To gain a deeper understanding of the IFN-I response, we compared the interferomes of IFNα subtypes dominantly-expressed in HIV-1-exposed plasmacytoid dendritic cells (1, 2, 5, 8 and 14) and IFNβ in the earliest cellular targets of HIV-1 infection. Primary gut CD4 T cells from 3 donors were treated for 18 hours ex vivo with individual IFN-Is normalized for IFNAR signaling strength. Of 1,969 IFN-regulated genes, 246 'core ISGs' were induced by all IFN-Is tested. However, many IFN-regulated genes were not shared between the IFNα subtypes despite similar induction of canonical antiviral ISGs such as ISG15, RSAD2 and MX1, formally demonstrating qualitative differences between the IFNα subtypes. Notably, IFNβ induced a broader interferome than the individual IFNα subtypes. Since IFNβ, and not IFNα, is upregulated during chronic HIV-1 infection in the gut, we compared core ISGs and IFNβ-specific ISGs from colon pinch biopsies of HIV-1-uninfected (n = 13) versus age- and gender-matched, antiretroviral-therapy naïve persons with HIV-1 (PWH; n = 19). Core ISGs linked to inflammation, T cell activation and immune exhaustion were elevated in PWH, positively correlated with plasma lipopolysaccharide (LPS) levels and gut IFNβ levels, and negatively correlated with gut CD4 T cell frequencies. In sharp contrast, IFNβ-specific ISGs linked to protein translation and anti-inflammatory responses were significantly downregulated in PWH, negatively correlated with gut IFNβ and LPS, and positively correlated with plasma IL6 and gut CD4 T cell frequencies. Our findings reveal qualitative differences in interferome induction by diverse IFN-Is and suggest potential mechanisms for how IFNβ may drive HIV-1 pathogenesis in the gut.

Project description:This study demonstrates quantitative and qualitative differences between type I IFN signatures in autoimmunity and viral infection using purified CD4pos T cells and CD16pos- and CD16neg-monocyte subsets. We were able to discriminate between cell-specific viral response signatures and the pathogenically amplified IFN signatures observed in autoimmunity. The differences were of both a qualitative and quantitative nature, as the signatures in the patients with SLE were characterized by much more complexly compiled gene patterns with increased absolute gene expression levels. Collection of cells from Systemic Lupus Erythematosus (SLE) patients: For CD4pos T cells, six patients with SLE (average age: 29.0 +/- 7.6) and four normal healthy donors (ND; 24.8 +/- 0.5) were recruited. For CD16neg monocytes, four patients with SLE (26.5 +/- 1.7) and four NDs (24.8 +/- 0.5) were recruited. For CD16pos monocytes, four patients with SLE (26.5 +/- 1.7) and three NDs (24.7 +/- 0.6) were recruited. All patients and NDs were female. The same NDs were examined before and after immunisation with yellow fever vaccine (YFV). Collection of cells from yellow-fever vaccinated individuals: ND were immunised with a vaccine against the wild-type YF virus, which is a single-stranded RNA virus without adjuvants. This vaccine consists of a live but attenuated strain of the yellow fever virus (YFV-17D). Based on its vaccination-associated clinical and serological manifestations, this immunisation can be regarded as a real viral infection. A total of 50 ml peripheral blood was taken 7 days after immunisation, when sufficient numbers of CD19pos/CD27high plasmablasts were detected by flow cytometry. Cell sorting: A total of 50 ml peripheral blood was collected in Vacutainer heparin tubes and erythrocytes were lysed in EL buffer. Subsequently, granulocytes were depleted using CD15-conjugated microbeads (MACS). The CD15-depleted fraction was stained with a CD14-fluorescein isothiocyanate (FITC) antibody, a CD16-APC-Cy7 antibody, a CD3-Vioblue antibody and a CD4-FITC antibody. Using a FACSAria cell sorter, CD4pos T cells, CD16neg monocytes and CD16pos monocytes were isolated with purities and viabilities of >97%. After sorting, the cells were immediately lysed with RLT buffer and frozen at -70 M-BM-0C. Total RNA was isolated using an RNeasy mini kit, and quality control was ensured by Bioanalyser measurements.Total RNA was extracted using the RNeasy Mini kit. The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer and a NanoDrop ND-1000 spectrophotometer. Biotinylated complementary RNA (cRNA) was synthesized from 100 ng total RNA, using reagents as recommended in the technical manual from Affymetrix. Fifteen micrograms of fragmented cRNA of each sample were hybridized to HG-U133 plus 2.0 arrays. Hybridization was performed according to procedure 2 as described in the technical manual. Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously.

Dataset Information

Differential levels of IFNα subtypes in autoimmunity and viral infection.

Publications

Differential levels of IFNα subtypes in autoimmunity and viral infection.

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