We present a comprehensive analysis of the human methyltransferasome. Primary sequences, predicted secondary structures, and solved crystal structures of known methyltransferases were analyzed by hidden Markov models, Fisher-based statistical matrices, and fold recognition prediction-based threading algorithms to create a model, or profile, of each methyltransferase superfamily. These profiles were used to scan the human proteome database and detect novel methyltransferases. 208 proteins in the human genome are now identified as known or putative methyltransferases, including 38 proteins that were not annotated previously. To date, 30% of these proteins have been linked to disease states. Possible substrates of methylation for all of the SET domain and SPOUT methyltransferases as well as 100 of the 131 seven-␤-strand methyltransferases were surmised from sequence similarity clusters based on alignments of the substrate-specific domains. Molecular & Cellular Proteomics 10: 10.1074/mcp.M110.000976, 1-12, 2011.A significant percentage of proteins across all organisms are enzymes that catalyze the transfer of a methyl group from the cofactor S-adenosylmethionine to a substrate (1-5). In yeast, these proteins make up about 1.2% of all gene products (6, 7). The ability of methyltransferases to use a variety of different substrates, including RNA, DNA, lipids, small molecules, and proteins, is responsible for their diverse roles in different biological pathways (1-3). Methyltransferases have been shown to be essential in epigenetic control, lipid biosynthesis, protein repair, hormone inactivation, and tissue differentiation (8 -14). The identification of new enzymes may allow the delineation of additional pathways and modes of regulation as well as increase our understanding of S-adenosylmethionine metabolism.Although there are hundreds of known substrates for these reactions, methyltransferases are found in a small number of distinct structural arrangements that are used to classify them into superfamilies (2,3,5,6,15). Proteins in each superfamily also share conserved amino acid sequences. The seven-␤-strand superfamily (also referred to as "Class I" methyltransferases) is the most abundant. These proteins catalyze a wide array of substrates and feature a Rossmann-like structural core (2,3,5,6,15). The SPOUT methyltransferase superfamily contains a distinctive knot structure and methylates RNA substrates (16). SET domain methyltransferases catalyze the methylation of protein lysine residues with histones and ribosomal proteins as major targets (17)(18)(19). Smaller superfamilies with at least one three-dimensional structure available include the precorrin-like methyltransferases (20), the radical SAM 1 methyltransferases (21, 22), the MetH activation domain (23), the Tyw3 protein involved in wybutosine synthesis (24), and the homocysteine methyltransferases (25-27). Lastly, an integral membrane methyltransferase family has been defined by sequence alone where no three-dimensional structure is yet available (28,29).Ad...

BackgroundAsthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear.ObjectiveWe performed a comprehensive assessment of TH17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients.MethodsSixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed.ResultsSignificant heterogeneity of T-cell phenotypes was observed, with levels of IL-13–secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. TH17 cells and γδ-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine–high) and nonatopic forms.ConclusionThe evidence for a role for TH17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

A new program (Multiple Motif Scanning) was developed to scan the Saccharomyces cerevisiae proteome for Class I S-adenosylmethionine-dependent methyltransferases. Conserved Motifs I, Post I, II, and III were identified and expanded in known methyltransferases by primary sequence and secondary structural analysis through hidden Markov model profiling of both a yeast reference database and a reference database of methyltransferases with solved three-dimensional structures. The roles of the conserved amino acids in the four motifs of the methyltransferase structure and function were then analyzed to expand the previously defined motifs. Fisher-based negative log statistical matrix sets were developed from the prevalence of amino acids in the motifs. Multiple Motif Scanning is able to scan the proteome and score different combinations of the top fitting sequences for each motif. In addition, the program takes into account the conserved number of amino acids between the motifs. The output of the program is a ranked list of proteins that can be used to identify new methyltransferases and to reevaluate the assignment of previously identified putative methyltransferases. The Multiple Motif Scanning program can be used to develop a putative list of enzymes for any type of protein that has one or more motifs conserved at variable spacings and is freely available (www.chem.ucla

Summary Methylation of DNA, protein, and even RNA species are integral processes in epigenesis. Enzymes that catalyze these reactions using the donor S-adenosylmethionine fall into several structurally distinct classes. The members in each class share sequence similarity that can be used to identify additional methyltransferases. Here, we characterize these classes and in silico approaches to infer protein function. Computational methods such as hidden Markov model profiling and the Multiple Motif Scanning program can be used to analyze known methyltransferases and relay information into the prediction of new ones. In some cases, the substrate of methylation can be inferred from hidden Markov model sequence similarity networks. Functional identification of these candidate species is much more difficult; we discuss one biochemical approach.