Mu Dan Pi (MDP), also known as Moutan Cortex Radicis, is a traditional Chinese medicine used to treat autoimmune diseases. However, the impact of MDP and its principal active compounds on inflammatory bowel disease (IBD) is uncertain. This study therefore systemically assessed the anti-inflammatory effects of MDP and its known active compounds in IBD. The anti-inflammatory activities of water extract and individual compounds were screened by NF-κB and interferon regulatory factor (IRF) reporter assays in THP-1 cells induced with either Toll-like receptor or retinoic acid inducible gene I/melanoma differentiation-associated gene 5 activators and further verified in bone marrow-derived macrophages. MDP water extract significantly inhibited the activation of NF-κB and IRF reporters, downstream signaling pathways and the production of IL-6 and TNF-α, in a dose-dependent manner. Among 5 known active components identified from MDP (1,2,3,4,6-penta-O-galloyl-β-d-glucose [PGG], gallic acid, methyl gallate, paeoniflorin, and paeonol), PGG was the most efficient at inhibiting both reporters (with an IC50 of 5–10 µM) and downregulating IL-6 and TNF-α. Both MDP powder for clinical use and MDP water extract, but not PGG, reduced colitis and pathological changes in mice. MDP and its water extract show promise as a novel therapy for IBD patients.
BackgroundIdentifying key components in biological processes and their associations is critical for deciphering cellular functions. Recently, numerous gene expression and molecular interaction experiments have been reported in Saccharomyces cerevisiae, and these have enabled systematic studies. Although a number of approaches have been used to predict gene functions and interactions, tools that analyze the essential coordination of functional components in cellular processes still need to be developed.ResultsIn this work, we present a new approach to study the cooperation of functional modules (sets of functionally related genes) in a specific cellular process. A cooperative module pair is defined as two modules that significantly cooperate with certain functional genes in a cellular process. This method identifies cooperative module pairs that significantly influence a cellular process and the correlated genes and interactions that are essential to that process. Using the yeast cell cycle as an example, we identified 101 cooperative module associations among 82 modules, and importantly, we established a cell cycle-specific cooperative module network. Most of the identified module pairs cover cooperative pathways and components essential to the cell cycle. We found that 14, 36, 18, 15, and 20 cooperative module pairs significantly cooperate with genes regulated in early G1, late G1, S, G2, and M phase, respectively. Fifty-nine module pairs that correlate with Cdc28 and other essential regulators were also identified. These results are consistent with previous studies and demonstrate that our methodology is effective for studying cooperative mechanisms in the cell cycle.ConclusionsIn this work, we propose a new approach to identifying condition-related cooperative interactions, and importantly, we establish a cell cycle-specific cooperation module network. These results provide a global view of the cell cycle and the method can be used to discover the dynamic coordination properties of functional components in other cellular processes.
The identification of regulatory elements recognized by transcription factors and chromatin remodeling factors is essential to studying the regulation of gene expression. When no auxiliary data, such as orthologous sequences or expression profiles, are used, the accuracy of most tools for motif discovery is strongly influenced by the motif degeneracy and the lengths of sequence. Since suitable auxiliary data may not always be available, more work must be conducted to enhance tool performance to identify transcription elements in the metazoan. A non-alignment-based algorithm, MotifSeeker, is proposed to enhance the accuracy of discovering degenerate motifs. MotifSeeker utilizes the property that variable sites of transcription elements are usually position-specific to reduce exposure to noise. Consequently, the efficiency and accuracy of motif identification are improved. Using data fusion, the ranking process integrates two measures of motif significance, resulting in a more robust significance measure. Testing results for the synthetic data reveal that the accuracy of MotifSeeker is less sensitive to the motif degeneracy and the length of input sequences. Furthermore, MotifSeeker has been tested on a well-known benchmark [M. Tompa, N. Li, T.L. Bailey, G.M. Church, B. De Moor, E. Eskin, A.V. Favorov, M.C. Frith, Y. Fu, W.J. Kent, et al. (2005) Nat. Biotechnol., 23, 137–144], yielding a correlation coefficient of 0.262, which compares favorably with those of other tools. The high applicability of MotifSeeker to biological data is further demonstrated experimentally on regulons of Saccharomyces cerevisiae and liver-specific genes with experimentally verified regulatory elements.
An efficient host immune response against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2, COVID‐19) appears to be crucial for controlling and resolving this viral infection. However, many studies have reported autoimmune characteristics in severe COVID‐19 patients. Moreover, clinical observations have revealed that COVID‐19‐associated acute distress respiratory syndrome shares many features in common with inflammatory myopathy including interstitial lung disease (ILD), most particularly rapidly progressive (RP)‐ILD. This study explored this phenomenon by seeking to identify and characterize myositis‐specific and related autoantibodies in 25 COVID‐19 patients with mild or severe symptoms. Line blot analysis with the EUROLINE Myopathies Ag kit identified 9 (36%) patients with COVID‐19 with one or more autoantibodies against several myositis‐related antigens (Jo‐1, Ku, Mi‐2β, PL‐7, PL‐12, PM‐Scl 75, PM‐Scl 100, Ro‐52, and SRP); no anti‐MDA5 antibodies were detected. As the presence of antibodies identified by line blots was unrelated to disease severity, we further characterized the autoantibodies by radioimmunoassay, in which [35S]methionine‐labeled K562 cellular antigens were precipitated and visualized by gel electrophoresis. This result was confirmed by an immunoprecipitation assay and immunoblotting; 2 patients exhibited anti‐Ku70 and anti‐Ku80 antibodies. Our data suggest that it is necessary to use more than one method to characterize and evaluate autoantibodies in people recovered from COVID‐19, in order to avoid misinterpreting those autoantibodies as diagnostic markers for autoimmune diseases.
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