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    Comparative analysis reveals a role for TGF-β in shaping the residency-related transcriptional signature in tissue-resident memory CD8+ T cells
    Nath, AP ; Braun, A ; Ritchie, SC ; Carbone, FR ; Mackay, LK ; Gebhardt, T ; Inouye, M ; Gangopadhyay, N (PUBLIC LIBRARY SCIENCE, 2019-02-11)
    Tissue-resident CD8+ memory T (TRM) cells are immune cells that permanently reside at tissue sites where they play an important role in providing rapid protection against reinfection. They are not only phenotypically and functionally distinct from their circulating memory counterparts, but also exhibit a unique transcriptional profile. To date, the local tissue signals required for their development and long-term residency are not well understood. So far, the best-characterised tissue-derived signal is transforming growth factor-β (TGF-β), which has been shown to promote the development of these cells within tissues. In this study, we aimed to determine to what extent the transcriptional signatures of TRM cells from multiple tissues reflects TGF-β imprinting. We activated murine CD8+ T cells, stimulated them in vitro by TGF-β, and profiled their transcriptomes using RNA-seq. Upon comparison, we identified a TGF-β-induced signature of differentially expressed genes between TGF-β-stimulated and -unstimulated cells. Next, we linked this in vitro TGF-β-induced signature to a previously identified in vivo TRM-specific gene set and found considerable (>50%) overlap between the two gene sets, thus showing that a substantial part of the TRM signature can be attributed to TGF-β signalling. Finally, gene set enrichment analysis further revealed that the altered gene signature following TGF-β exposure reflected transcriptional signatures found in TRM cells from both epithelial and non-epithelial tissues. In summary, these findings show that TGF-β has a broad footprint in establishing the residency-specific transcriptional profile of TRM cells, which is detectable in TRM cells from diverse tissues. They further suggest that constitutive TGF-β signaling might be involved for their long-term persistence at tissue sites.
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    Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality
    Ritchie, SC ; Kettunen, J ; Brozynska, M ; Nath, AP ; Havulinna, AS ; Maennist, S ; Perola, M ; Salomaa, V ; Ala-Korpela, M ; Abraham, G ; Wuertz, P ; Inouye, M ; Feng, Y-M (PUBLIC LIBRARY SCIENCE, 2019-10-23)
    BACKGROUND: GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown. METHODS: We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT. RESULTS: Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10-10), influenza and pneumonia (HR = 1.37, P = 6×10-10), and liver diseases (HR = 1.81, P = 1×10-6). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways. CONCLUSIONS: This study clarifies the molecular underpinnings of the GlycA biomarker's associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.