Identification of Novel Nuclear Factor of Activated T Cell (NFAT)-associated Proteins in T Cells.

Transcription factors of the nuclear factor of activated T cell (NFAT) family are essential for antigen-specific T cell activation and differentiation. Their cooperative DNA binding with other transcription factors, such as AP1 proteins (FOS, JUN, and JUNB), FOXP3, IRFs, and EGR1, dictates the gene regulatory action of NFATs. To identify as yet unknown interaction partners of NFAT, we purified biotin-tagged NFATc1/αA, NFATc1/ßC, and NFATc2/C protein complexes and analyzed their components by stable isotope labeling by amino acids in cell culture-based mass spectrometry. We revealed more than 170 NFAT-associated proteins, half of which are involved in transcriptional regulation. Among them are many hitherto unknown interaction partners of NFATc1 and NFATc2 in T cells, such as Raptor, CHEK1, CREB1, RUNX1, SATB1, Ikaros, and Helios. The association of NFATc2 with several other transcription factors is DNA-dependent, indicating cooperative DNA binding. Moreover, our computational analysis discovered that binding motifs for RUNX and CREB1 are found preferentially in the direct vicinity of NFAT-binding motifs and in a distinct orientation to them. Furthermore, we provide evidence that mTOR and CHEK1 kinase activity influence NFAT's transcriptional potency. Finally, our dataset of NFAT-associated proteins provides a good basis to further study NFAT's diverse functions and how these are modulated due to the interplay of multiple interaction partners.

Transcription factor co-occupied regions in the murine genome constitute T-helper-cell subtype-specific enhancers.

Transcription factors (TFs) regulate cell-type-specific gene expression programs by combinatorial binding to cis-genomic elements, particularly enhancers, subsequently leading to the recruitment of cofactors, and the general transcriptional machinery to target genes. Using data integration of genome-wide TF binding profiles, we defined regions with combinatorial binding of lineage-specific master TFs (T-BET, GATA3, and ROR-γt) and STATs (STAT1 and STAT4, STAT6, and STAT3) in murine T helper (Th) 1, Th2, and Th17 cells, respectively. Stringently excluding promoter regions, we revealed precise genomic elements which were preferentially associated with the enhancer marks p300 and H3K4me1. Furthermore, closely adjacent TF co-occupied regions constituted larger enhancer domains in the respective Th-cell subset (177 in Th1, 141 in Th2, and 266 in Th17 cells) with characteristics of so-called super-enhancers. Importantly, 89% of these super-enhancer regions were Th-cell subtype-specific. Genes associated with super-enhancers, including relevant Th-cell genes (such as Ifng in Th1, Il13 in Th2, and Il17a in Th17 cells), showed strong transcriptional activity. Altogether, the discovered catalog of enhancers provides information about crucial Th-cell subtype-specific regulatory hubs, which will be useful for revealing cell-type-specific gene regulation processes.

Network integration and modelling of dynamic drug responses at multi-omics levels.

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data.