A molecular network regulating the proinflammatory phenotype of human memory T lymphocytes

Abstract

Understanding the mechanisms that modulate helper T lymphocyte functions is crucial to decipher normal and pathogenic immune responses in humans. To identify molecular determinants influencing the pathogenicity of T cells, we separated ex vivo-isolated primary human memory T lymphocytes on the basis of their ability to produce high levels of inflammatory cytokines. We found that the inflammatory, cytokine-producing phenotype of memory T lymphocytes was defined by a specific core gene signature and was mechanistically regulated by the constitutive activation of the NF-κB pathway and by the expression of the transcriptional repressor BHLHE40. BHLHE40 attenuated the expression of anti-inflammatory factors, including miR-146a, a negative regulator of NF-κB activation and ZC3H12D, an RNase of the Regnase-1 family able to degrade inflammatory transcripts. Our data reveal a molecular network regulating the proinflammatory phenotype of human memory T lymphocytes, with the potential to contribute to disease.

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Fig. 1: Transcriptomic analysis of GM-CSF+ and GM-CSF cells.
Fig. 2: A general gene signature linked to a high cytokine-producing phenotype.
Fig. 3: Identification of TFs regulating the inflammatory cytokine-producing phenotype.
Fig. 4: BHLHE40 affects inflammatory cytokine production in human memory T cells.
Fig. 5: Phosphorylation of NF-κB p65 in GM-CSF+ and GM-CSF T cells.
Fig. 6: miR-146a expression is regulated by BHLHE40 and inhibits NF-κB p65 phosphorylation.
Fig. 7: ChIP-seq of BHLHE40 identifies specific binding loci in the human genome.
Fig. 8: Direct regulation of ZC3H12D by BHLHE40 and effect on inflammatory cytokine expression in human T lymphocytes.

Data availability

All Nanostring, RNA-seq, ChIP-seq and ATAC-seq datasets are available in GEO (accession number GSE122946). Source data are provided for Figs. 4e and 5b. Other supporting raw data are available from the corresponding author upon request.

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Acknowledgements

The authors thank D. Jarossay, S. Notarbartolo and F. Mele for invaluable technical support and input; M. Perez for help with Fig. 1; L. Vincenzetti for pilot CRISPR-Cas9 experiments and E. Džafo for help with experiments involving Regnase-4. This work was supported by the Swiss National Science Foundation (grant number 156875 and 175569), the NCCR ‘RNA & Disease’, the Swiss MS Society, the Ceresio Foundation, the Vontobel Stiftung and the Kurt und Senta Herrmann Stiftung (all to S. Monticelli). S. Montagner was supported by a Marie Heim-Vögtlin postdoctoral fellowship (number 164489). This work was also partially supported by the Italian Ministry of Health with Ricerca Corrente and 5×1000 funds (to G.N.).

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S.E., N.B., C.L., S. Montagner and M.C. designed and performed experiments and analyzed data; S.P., C.B. and G.N. performed and analyzed all the sequencing experiments; N.D. analyzed data; S. Monticelli oversaw the project and analyzed data. S. Monticelli and G.N. wrote the manuscript with input from all authors.

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Correspondence to Silvia Monticelli.

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Extended data

Extended Data Fig. 1 Sorting scheme and principal component analysis.

a, Sorting scheme for GM-CSF secretion assay. TEM cells were separated by sorting and further divided by secretion assay and sorting. b, Principal component analysis of RNA-seq data. Cells from nine individual donors were separated by GM-CSF secretion assay and pooled in three pools of three donors each prior to RNA-seq. This analysis showed robust results, with differences due to sample-to-sample variability accounting for only 15.45% (PC#2) of the variance, while the majority of the observed differences were due to the phenotype of the cells (65.03%, GM-CSFpos vs. neg, PC#1).

Extended Data Fig. 2 Expression of specific markers in T cell subsets.

a, Cytokine expression in different T cell subsets. Each indicated cytokine (IL-22, IL-17A and IFN-γ) is expressed with or without concomitant co-expression of GM-CSF. Also, the IL-22+ cells are not simply a subset of the larger GM-CSF+ pool of cells. Mean ± SD. Each dot represents one donor (n = 5). b, Expression of IL23R. Expression of the IL23R gene in GM-CSF+ and GM-CSF cells, as determined by RNA-seq (n = 9). FDR = False discovery rate calculated for RNA-seq samples. c, FOXP3 expression. Comparison of FOXP3 expression in Treg cells (CD4+CD25highCD127low), GM-CSF+ and GM-CSF cells, as assessed by intracellular staining. Representative of n = 2 experiments.

Extended Data Fig. 3 Differences in gene expression patterns between GM-CSF+ and GM-CSF TEM cells.

Systematic delineation of coordinated changes in gene expression within specific gene sets (that is, pathways) was achieved by applying the data analysis and data visualization pipeline described in the Methods. 119 gene sets yielded significant enrichment in GM-CSF+ vs. GM-CSF cells (false-discovery-rate (FDR)-q < 0.01, nom. p-value of < 0.005 and TAGS > 50) whereas none were found depleted. Defining the network’s organization is the degree of overlap in leading edge-genes (genes contributing to each gene set’s significant enrichment) two gene sets (nodes) linked by a connection (edge) share (threshold connectivity 0.05). Node-size corresponds to FDR-q value threshold passed by the gene set (small < 0.01, medium < 0.001, large < 0.0001). Node color denotes MCL-cluster membership that subsequently would be summarized within 5 biological themes that is color-coded network areas. Their FDR-q value distribution is displayed in Fig. 1f.

Extended Data Fig. 4 RNA-seq, Nanostring, ATAC-seq and ChIP-seq analyses.

a, Comparison of RNA-seq and Nanostring data. Concordant results of gene expression profiling obtained by RNA-seq of TEM cells and Nanostring profiling of TCM cells. b, Expression of BHLHE40 in different T cell subsets. Expression of BHLHE40 was determined by qRT-PCR in different T cell subsets freshly sorted from peripheral blood. Each dot represents one donor (n = 6). Mean ± SD; paired t-test, two-tailed. c, ATAC-seq and ChIP-seq analyses. Representative snapshots of ChIP-seq and ATAC-seq tracks for selected genomic loci.

Extended Data Fig. 5 Optimization of CRISP-Cas9-mediated deletion and screening.

a, As a proof-of-principle and test of efficiency, Jurkat T cells were transfected using the Neon transfection system with ribonucleoparticles of Cas9 and one gRNA against the TCRα chain. After three days the cells were stained for surface TCR expression, showing a loss of expression in 62% of the cells (green). Grey: mock transfected control cells. Representative of at least n = 3 independent experiments. b, Schematic representation of the BHLHE40 locus with indicated the location of gRNAs and primers used for screening of the clones. c, Optimization of screening procedure by mismatch cleavage assay. After transfection with two gRNAs against BHLHE40, Jurkat cells were single cell-cloned by seeding in a 384-well plate in 20% FBS. Genomic DNA was extracted from 10 clones and from the entire population (no cloning) and negative controls. The first ‘long’ PCR showed already the presence of indels in most clones (left panel) and even at the level of whole population. This was further confirmed by denaturing, reannealing and T7 endonuclease digestion (middle panel). The presence of a homozygous deletion was further confirmed using a ‘short’ PCR that cannot provide an amplification product if the region between the two gRNAs is deleted. The highlighted Clone 7 (black box) is one of several clones in which the deletion was most likely identical on both alleles, leading to a shorter PCR product that cannot be digested by the T7 endonuclease because of the absence of mismatches. Representative of at least n = 2 independent transfections and cloning procedures. d, The genomic DNA from Clone 7 was sequenced in the BHLHE40 locus region, revealing a deletion of 196 nucleotides involving part of exon 1 and exon 2, as well as the intron. e, Schematic representation of the ZC3H12D locus with indicated the location of gRNAs and primers used for screening of the clones in Fig. 8f.

Extended Data Fig. 6 Analysis of primary human T cell clones transfected with Cas9 ribonucleoparticles directed against the BHLHE40 gene.

Primary human memory T lymphocytes were transfected with ribonucleoparticles of Cas9 and gRNAs against exon 1/2 and exon 5 of BHLHE40. After single cell cloning and expansion, individual clones were tested for the presence of the deletion by PCR. Median; Mann-Whitney test. Each dot represents one clone (n = 77 for KO and n = 90 for control clones).

Extended Data Fig. 7 MiRNA analysis.

a, Transduction with a miR-146a sponge of primary human T lymphocytes leads to increased NF-kB p65 phosphorylation. Primary memory T lymphocytes were transduced with a lentivirus expressing a miR-146a sponge and GFP as an independent reporter of the efficiency of transduction (schematic representation on top); a few days after transduction cells were stimulated with PMA and ionomycin for 5 min and levels of p65 phosphorylation were determined by intracellular staining. Cells were gated as GFPneg (non-expressing the sponge) and GFPpos (expressing the sponge) and compared also to untransduced cells. Representative of n = 2 experiments. b, Same as (c) except that the results (expressed as mean fluorescence intensity, MFI) of two independent experiments in which cells were stimulated for the indicated times are shown. c, Effect of BHLHE40 expression on miR-181a in Jurkat T cells. Jurkat T cells were transduced with a BHLHE40-expressing lentivirus. After selection and expansion of the transduced cells, expression of miR-181a was measured by qRT-PCR. Each dot represents one independent experiment (n = 4). Mean ± SD; paired t-test, two-tailed. d, Phylogenetic analysis of the top 50 bHLH matrices recovered from the BHLHE40 ChIP-seq data.

Extended Data Fig. 8 Schematic diagram describing the regulatory module identified in this study.

Using GM-CSF secretion as a proxy for an inflammatory phenotype of primary human memory TH lymphocytes, we identified a regulatory module that influences the activity of these pro-inflammatory and potentially pathogenic cells. In GM-CSF cells, mechanisms are in place to actively repress the expression of inflammatory cytokines, including high levels of miR-146a expression (a negative regulator of NF-κB activation), and relatively higher expression of ZC3H12D, an RNase enzyme involved in the negative regulation of cytokine expression. Conversely, in GM-CSF+ cells, the expression of the transcriptional repressor BHLHE40 leads to the direct downregulation of ZC3H12D expression, and indirectly affects also miR-146a. This in turn allows full-blown NF-κB activation and expression of inflammatory cytokines.

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Source Data Fig. 4

Uncropped images of western blots in Fig. 4. After transfer, the membrane was cut using the marker as a reference and the two parts of the same membrane were blotted with the indicated antibody. The boxes represent the parts of the gel shown in the figure.

Source Data Fig. 5

Uncropped images of western blots in Fig. 4. After transfer, the membrane was cut using the marker as a reference and the two parts of the same membrane were blotted with the indicated antibody. For each experiment, the same lysate was loaded twice on the same gel, the membrane was cut and part of the membrane was blotted with an anti-phosphorylated-p65 antibody, while the other part was blotted with a total anti-p65 antibody. The boxes represent the parts of the gels shown in the figure.

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Emming, S., Bianchi, N., Polletti, S. et al. A molecular network regulating the proinflammatory phenotype of human memory T lymphocytes. Nat Immunol 21, 388–399 (2020). https://doi.org/10.1038/s41590-020-0622-8

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