No abstract

SUMMARY Stringent control of NF-κB and mitogen-activated protein kinase (MAPK) signaling is critical during innate immune responses. TGF-β activated kinase-1 (TAK1) is essential for NF-κB activation in T and B cells but has precisely the opposite activity in myeloid cells. Specific deletion of TAK1 (Map3k7ΔM/ΔM) led to development of splenomegaly and lymphomegaly associated with neutrophilia. Compared with wild-type cells, TAK1-deficient neutrophils enhanced the phosphorylation of the kinases IKK, p38, and JNK and the production of interleukin-1β (IL-1β), IL-6, tumor necrosis factor-α (TNF-α), and reactive oxygen species (ROS) after lipopolysaccharide (LPS) stimulation. Map3k7ΔM/ΔM mice were significantly more susceptible to LPS-induced septic shock and produced higher amounts of IL-1β, IL-6, and TNF-α in plasma than do wild-type mice. Specific ablation of p38 rescued the phenotype and functional properties of Map3k7ΔM/ΔM mice. Our findings identify a previously unrecognized role of TAK1 as a negative regulator of p38 and IKK activation in a cell type-specific manner.

Cancer is a leading cause of death worldwide; due to the lack of ideal cancer biomarkers for early detection or diagnosis, most patients present with late-stage disease at the time of diagnosis, thus limiting the potential for successful treatment. Traditional cancer treatments, including surgery, chemotherapy and radiation therapy, have demonstrated very limited efficacy for patients with late-stage disease. Therefore, innovative and effective cancer treatments are urgently needed for cancer patients with late-stage and refractory disease. Cancer immunotherapy, particularly adoptive cell transfer, has shown great promise in the treatment of patients with late-stage disease, including those who are refractory to standard therapies. In this review, we will highlight recent advances and discuss future directions in adoptive cell transfer based cancer immunotherapy.

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed ‘PredPPCrys’ using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.