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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.

ObjectivesTo identify novel DNA methylation sites significant for rheumatoid arthritis (RA) and comprehensively understand their underlying pathological mechanism.MethodsWe performed (1) genome-wide DNA methylation and mRNA expression profiling in peripheral blood mononuclear cells from RA patients and health controls; (2) correlation analysis and causal inference tests for DNA methylation and mRNA expression data; (3) differential methylation genes regulatory network construction; (4) validation tests of 10 differential methylation positions (DMPs) of interest and corresponding gene expressions; (5) correlation between PARP9 methylation and its mRNA expression level in Jurkat cells and T cells from patients with RA; (6) testing the pathological functions of PARP9 in Jurkat cells.ResultsA total of 1046 DNA methylation positions were associated with RA. The identified DMPs have regulatory effects on mRNA expressions. Causal inference tests identified six DNA methylation–mRNA–RA regulatory chains (eg, cg00959259-PARP9-RA). The identified DMPs and genes formed an interferon-inducible gene interaction network (eg, MX1, IFI44L, DTX3L and PARP9). Key DMPs and corresponding genes were validated their differences in additional samples. Methylation of PARP9 was correlated with mRNA level in Jurkat cells and T lymphocytes isolated from patients with RA. The PARP9 gene exerted significant effects on Jurkat cells (eg, cell cycle, cell proliferation, cell activation and expression of inflammatory factor IL-2).ConclusionsThis multistage study identified an interferon-inducible gene interaction network associated with RA and highlighted the importance of PARP9 gene in RA pathogenesis. The results enhanced our understanding of the important role of DNA methylation in pathology of RA.

Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.

MIPD is technically feasible and safe in highly selected patients and can offer acceptable oncological outcomes. But concerns such as long-term outcomes, cost-effectiveness analysis, and learning curve analysis should be fully demonstrated before the popularization of this challenging procedure.

A novel polyelectrolyte complex (PEC) formed by sodium cellulose sulfate (NaCS) and chitosan was prepared as a potential material for a colon-specific drug delivery system. The characteristics of NaCS-chitosan film were measured by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), in vitro degradation, and in vitro drug release experiments. SEM data indicated that the NaCS-chitosan film had relatively homogeneous and smooth morphology at the initial state and deformed after being immersed in simulated colonic fluid (SCF). FTIR data indicated that the NH 3 + of the chitosan had reacted with the SO 4of the NaCS. In vitro degradation behavior revealed that NaCS-chitosan could be degraded by colon microflora and be hydrolyzed in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF). It is found in the experiment of in vitro drug release that the capsules formed by the NaCS-chitosan complex could release about 80% of the drug loaded in the SCF during 4 h. All these results indicated that the NaCS-chitosan complex shows excellent behavior for colon specificity and could be a potential material for a colon-specific drug delivery system.