The CD4+ and CD8+ T cell dichotomy is essential for effective cellular immunity. How the individual T cell identity is established remains poorly understood. Here we show that the high mobility group (HMG) transcription factors Tcf1 and Lef1 are essential for repressing CD4+ lineage-associated genes including Cd4, Foxp3 and Rorc in CD8+ T cells. Tcf1- and Lef1-deficient CD8+ T cells exhibit histone hyperacetylation, which is ascribed to an unexpected intrinsic histone deacetylase (HDAC) activity in Tcf1 and Lef1. Mutating five conserved amino acids in the Tcf1 HDAC domain diminishes the HDAC activity and the ability to suppress CD4+ lineage genes in CD8+ T cells. These findings reveal that sequence-specific transcription factors can utilize intrinsic HDAC activity to guard cell identity by repressing lineage-inappropriate genes.

Building tertiary structures of non-coding RNA is required to understand their functions and design new molecules. Current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation. Here, we present an automated and fast program,3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.

Model evaluation is a necessary step for better prediction and design of 3D RNA structures. For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem. Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures. The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs. Here, we present a novel knowledge-based potential, 3dRNAscore, which combines distance-dependent and dihedral-dependent energies. The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than existing evaluation methods in recognizing native state from a pool of near-native states of RNAs as well as in ranking near-native states of RNA models.

Major anti-inflammatory agents, steroids and cyclooxygenase, were proved to have serious side effects. Here, a series of chalcone derivatives were synthesized and screened for anti-inflammatory activities. QSAR study revealed that the presence of electron-withdrawing groups in B-ring and electron-donating groups in A-ring of chalcones was important for inhibition of LPS-induced IL-6 expression. Further, compounds 22, 23, 26, 40, and 47 inhibited TNF-α and IL-6 release in a dose-dependent manner and decreased LPS-induced TNF-α, IL-1β, IL-6, IL-12, and COX-2 mRNA production. Mechanistically, compounds 23 and 26 interfered with JNK/NF-κB signaling and dose-dependently prevented ERK and p38 activation. In addition, 23 and 26 exhibited a significant protection against LPS-induced death and were able to block high glucose-activated cytokine profiles in macrophages. Together, these data show a series of anti-inflammatory chalcones with potential therapeutic effects in inflammatory diseases.

Direct coupling analysis of nucleotide coevolution provides a novel approach to identify which nucleotides in an RNA molecule are likely in direct contact, and this information obtained from sequence only can be used to predict RNA 3D structures with much improved accuracy. Here we present an efficient method that incorporates this information into current RNA 3D structure prediction methods, specifically 3dRNA. Our method makes much more accurate RNA 3D structure prediction than the original 3dRNA as well as other existing prediction methods that used the direct coupling analysis. In particular our method demonstrates a significant improvement in predicting multi-branch junction conformations, a major bottleneck for RNA 3D structure prediction. We also show that our method can be used to optimize the predictions by other methods. These results indicate that optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide–nucleotide interactions from direct coupling analysis offers an efficient way for accurate RNA tertiary structure predictions.