Human T-bet Governs Innate and Innate-like Adaptive IFN-γ Immunity against Mycobacteria.

Inborn errors of human interferon gamma (IFN-γ) immunity underlie mycobacterial disease. We report a patient with mycobacterial disease due to inherited deficiency of the transcription factor T-bet. The patient has extremely low counts of circulating Mycobacterium-reactive natural killer (NK), invariant NKT (iNKT), mucosal-associated invariant T (MAIT), and Vδ2+ γδ T lymphocytes, and of Mycobacterium-non reactive classic TH1 lymphocytes, with the residual populations of these cells also producing abnormally small amounts of IFN-γ. Other lymphocyte subsets develop normally but produce low levels of IFN-γ, with the exception of CD8+ αß T and non-classic CD4+ αß TH1∗ lymphocytes, which produce IFN-γ normally in response to mycobacterial antigens. Human T-bet deficiency thus underlies mycobacterial disease by preventing the development of innate (NK) and innate-like adaptive lymphocytes (iNKT, MAIT, and Vδ2+ γδ T cells) and IFN-γ production by them, with mycobacterium-specific, IFN-γ-producing, purely adaptive CD8+ αß T, and CD4+ αß TH1∗ cells unable to compensate for this deficit.

Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation.

BACKGROUND: DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive. RESULTS: We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. CONCLUSION: Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array.

BACKGROUND: The capacity of technologies measuring DNA methylation (DNAm) is rapidly evolving, as are the options for applicable bioinformatics methods. The most commonly used DNAm microarray, the Illumina Infinium HumanMethylation450 (450K array), has recently been replaced by the Illumina Infinium HumanMethylationEPIC (EPIC array), nearly doubling the number of targeted CpG sites. Given that a subset of 450K CpG sites is absent on the EPIC array and that several tools for both data normalization and analyses were developed on the 450K array, it is important to assess their utility when applied to EPIC array data. One of the most commonly used 450K tools is the pan-tissue epigenetic clock, a multivariate predictor of biological age based on DNAm at 353 CpG sites. Of these CpGs, 19 are missing from the EPIC array, thus raising the question of whether EPIC data can be used to accurately estimate DNAm age. We also investigated a 71-CpG epigenetic age predictor, referred to as the Hannum method, which lacks 6 probes on the EPIC array. To evaluate these epigenetic clocks in EPIC data properly, a prior assessment of the effects of data preprocessing methods on DNAm age is also required. METHODS: DNAm was quantified, on both the 450K and EPIC platforms, from human primary monocytes derived from 172 individuals. We calculated DNAm age from raw, and three different preprocessed data forms to assess the effects of different processing methods on the DNAm age estimate. Using an additional cohort, we also investigated DNAm age of peripheral blood mononuclear cells, bronchoalveolar lavage, and bronchial brushing samples using the EPIC array. RESULTS: Using monocyte-derived data from subjects on both the 450K and EPIC, we found that DNAm age was highly correlated across both raw and preprocessing methods (r > 0.91). Thus, the correlation between chronological age and the DNAm age estimate is largely unaffected by platform differences and normalization methods. However, we found that the choice of normalization method and measurement platform can lead to a systematic offset in the age estimate which in turn leads to an increase in the median error. Comparing the 450K and EPIC DNAm age estimates, we observed that the median absolute difference was 1.44-3.10 years across preprocessing methods. CONCLUSIONS: Here, we have provided evidence that the epigenetic clock is resistant to the lack of 19 CpG sites missing from the EPIC array as well as highlighted the importance of considering the technical variance of the epigenetic when interpreting group differences below the reported error. Furthermore, our study highlights the utility of epigenetic age acceleration measure, the residuals from a linear regression of DNAm age on chronological age, as the resulting values are robust with respect to normalization methods and measurement platforms.