O-GlcNAc as an Integrator of Signaling Pathways

O-GlcNAcylation is an important posttranslational modification governed by a single pair of enzymes–O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). These two enzymes mediate the dynamic cycling of O-GlcNAcylation on a wide variety of cytosolic, nuclear and mitochondrial proteins in a nutrient- and stress-responsive fashion. While cellular functions of O-GlcNAcylation have been emerging, little is known regarding the precise mechanisms how the enzyme pair senses the environmental cues to elicit molecular and physiological changes. In this review, we discuss how the OGT/OGA pair acts as a metabolic sensor that integrates signaling pathways, given their capability of receiving signaling inputs from various partners, targeting multiple substrates with spatiotemporal specificity and translocating to different parts of the cell. We also discuss how the pair maintains homeostatic signaling within the cell and its physiological relevance. A better understanding of the mechanisms of OGT/OGA action would enable us to derive therapeutic benefits of resetting cellular O-GlcNAc levels within an optimal range

Fully Convolutional Networks for Continuous Sign Language Recognition

Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accomplishes this by using CNN and RNN hybrid networks. However, training these networks is generally non-trivial, and most of them fail in learning unseen sequence patterns, causing an unsatisfactory performance for online recognition. In this paper, we propose a fully convolutional network (FCN) for online SLR to concurrently learn spatial and temporal features from weakly annotated video sequences with only sentence-level annotations given. A gloss feature enhancement (GFE) module is introduced in the proposed network to enforce better sequence alignment learning. The proposed network is end-to-end trainable without any pre-training. We conduct experiments on two large scale SLR datasets. Experiments show that our method for continuous SLR is effective and performs well in online recognition.Comment: Accepted to ECCV202

Chloroplast genome sequencing analysis of Heterosigma akashiwo CCMP452 (West Atlantic) and NIES293 (West Pacific) strains

Background: Heterokont algae form a monophyletic group within the stramenopile branch of the tree of life. These organisms display wide morphological diversity, ranging from minute unicells to massive, bladed forms. Surprisingly, chloroplast genome sequences are available only for diatoms, representing two (Coscinodiscophyceae and Bacillariophyceae) of approximately 18 classes of algae that comprise this taxonomic cluster. A universal challenge to chloroplast genome sequencing studies is the retrieval of highly purified DNA in quantities sufficient for analytical processing. To circumvent this problem, we have developed a simplified method for sequencing chloroplast genomes, using fosmids selected from a total cellular DNA library. The technique has been used to sequence chloroplast DNA of two Heterosigma akashiwo strains. This raphidophyte has served as a model system for studies of stramenopile chloroplast biogenesis and evolution. Results: H. akashiwo strain CCMP452 (West Atlantic) chloroplast DNA is 160,149 bp in size with a 21,822-bp inverted repeat, whereas NIES293 (West Pacific) chloroplast DNA is 159,370 bp in size and has an inverted repeat of 21,665 bp. The fosmid cloning technique reveals that both strains contain an isomeric chloroplast DNA population resulting from an inversion of their single copy domains. Both strains contain multiple small inverted and tandem repeats, non-randomly distributed within the genomes. Although both CCMP452 and NIES293 chloroplast DNAs contains 197 genes, multiple nucleotide polymorphisms are present in both coding and intergenic regions. Several protein-coding genes contain large, in-frame inserts relative to orthologous genes in other plastids. These inserts are maintained in mRNA products. Two genes of interest in H. akashiwo, not previously reported in any chloroplast genome, include tyrC, a tyrosine recombinase, which we hypothesize may be a result of a lateral gene transfer event, and an unidentified 456 amino acid protein, which we hypothesize serves as a G-protein-coupled receptor. The H. akashiwo chloroplast genomes share little synteny with other algal chloroplast genomes sequenced to date. Conclusion: The fosmid cloning technique eliminates chloroplast isolation, does not require chloroplast DNA purification, and reduces sequencing processing time. Application of this method has provided new insights into chloroplast genome architecture, gene content and evolution within the stramenopile cluster

The ‘Singapore Fever’ in China: policy mobility and mutation

The ‘Singapore Model’ has constituted the only second explicit attempt by the Communist Party of China (CPC) to learn from a foreign country following Mao Zedong’s pledge to contour ‘China’s tomorrow’ on the Soviet Union experience during the early 1950s. This paper critically evaluates policy transfers from Singapore to China in the post-Mao era. It re-examines how this Sino-Singaporean regulatory engagement came about historically following Deng Xiaoping’s visit to Singapore in 1978, and offers a careful re-reading of the degree to which actual policy borrowing by China could transcend different state ideologies, abstract ideas and subjective attitudes. Particular focus is placed on the effects of CPC cadre training in Singapore universities and policy mutation within two government-to-government projects, namely the Suzhou Industrial Park and the Tianjin Eco-City. The paper concludes that the ‘Singapore Model’, as applied in post-Mao China, casts institutional reforms as an open-ended process of policy experimentation and adaptation that is fraught with tension and resistance

ESNOQ, Proteomic Quantification of Endogenous S-Nitrosation

S-nitrosation is a post-translational protein modification and is one of the most important mechanisms of NO signaling. Endogenous S-nitrosothiol (SNO) quantification is a challenge for detailed functional studies. Here we developed an ESNOQ (Endogenous SNO Quantification) method which combines the stable isotope labeling by amino acids in cell culture (SILAC) technique with the detergent-free biotin-switch assay and LC-MS/MS. After confirming the accuracy of quantification in this method, we obtained an endogenous S-nitrosation proteome for LPS/IFN-γ induced RAW264.7 cells. 27 S-nitrosated protein targets were confirmed and using our method we were able to obtain quantitative information on the level of S-nitrosation on each modified Cys. With this quantitative information, over 15 more S-nitrosated targets were identified than in previous studies. Based on the quantification results, we found that the S-nitrosation levels of different cysteines varied within one protein, providing direct evidence for differences in the sensitivity of cysteine residues to reactive nitrosative stress and that S-nitrosation is a site-specific modification. Gene ontology clustering shows that S-nitrosation targets in the LPS/IFN-γ induced RAW264.7 cell model were functionally enriched in protein translation and glycolysis, suggesting that S-nitrosation may function by regulating multiple pathways. The ESNOQ method described here thus provides a solution for quantification of multiple endogenous S-nitrosation events, and makes it possible to elucidate the network of relationships between endogenous S-nitrosation targets involved in different cellular processes

Gauged Flavor Group with Left-Right Symmetry

We construct an anomaly-free extension of the left-right symmetric model, where the maximal flavor group is gauged and anomaly cancellation is guaranteed by adding new vectorlike fermion states. We address the question of the lowest allowed flavor symmetry scale consistent with data. Because of the mechanism recently pointed out by Grinstein et al. tree-level flavor changing neutral currents turn out to play a very weak constraining role. The same occurs, in our model, for electroweak precision observables. The main constraint turns out to come from WR-mediated flavor changing neutral current box diagrams, primarily K - Kbar mixing. In the case where discrete parity symmetry is present at the TeV scale, this constraint implies lower bounds on the mass of vectorlike fermions and flavor bosons of 5 and 10 TeV respectively. However, these limits are weakened under the condition that only SU(2)_R x U(1)_{B-L} is restored at the TeV scale, but not parity. For example, assuming the SU(2) gauge couplings in the ratio gR/gL approx 0.7 allows the above limits to go down by half for both vectorlike fermions and flavor bosons. Our model provides a framework for accommodating neutrino masses and, in the parity symmetric case, provides a solution to the strong CP problem. The bound on the lepton flavor gauging scale is somewhat stronger, because of Big Bang Nucleosynthesis constraints. We argue, however, that the applicability of these constraints depends on the mechanism at work for the generation of neutrino masses.Comment: 1+23 pages, 1 table, 5 figures. v3: some more textual fixes (main change: discussion of Lepton Flavor Violating observables rephrased). Matches journal versio

Enzyme classification with peptide programs: a comparative study

<p>Abstract</p> <p>Background</p> <p>Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length.</p> <p>Results</p> <p>We have developed a machine learning methodology, called peptide programs (PPs), to deal directly with protein sequences and compared its performance with that of Support Vector Machines (SVMs) and BLAST in detailed enzyme classification tasks. Overall, the PPs and SVMs had a similar performance in terms of Matthews Correlation Coefficient, but the PPs had generally a higher precision. BLAST performed globally better than both methodologies, but the PPs had better results than BLAST and SVMs for the smaller datasets.</p> <p>Conclusion</p> <p>The higher precision of the PPs in comparison to the SVMs suggests that dealing with sequences is advantageous for detailed protein classification, as precision is essential to avoid annotation errors. The fact that the PPs performed better than BLAST for the smaller datasets demonstrates the potential of the methodology, but the drop in performance observed for the larger datasets indicates that further development is required.</p> <p>Possible strategies to address this issue include partitioning the datasets into smaller subsets and training individual PPs for each subset, or training several PPs for each dataset and combining them using a bagging strategy.</p